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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 6800a6ffac59c178770a0c0cc83712a8
+tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/2023.2/.doctrees/nbsphinx/samples/ElasticNet.ipynb b/2023.2/.doctrees/nbsphinx/samples/ElasticNet.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb b/2023.2/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/dbscan.ipynb b/2023.2/.doctrees/nbsphinx/samples/dbscan.ipynb
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/dbscan.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/kmeans.ipynb b/2023.2/.doctrees/nbsphinx/samples/kmeans.ipynb
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/kmeans.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/knn_mnist.ipynb b/2023.2/.doctrees/nbsphinx/samples/knn_mnist.ipynb
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/knn_mnist.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/lasso_regression.ipynb b/2023.2/.doctrees/nbsphinx/samples/lasso_regression.ipynb
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/lasso_regression.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/linear_regression.ipynb b/2023.2/.doctrees/nbsphinx/samples/linear_regression.ipynb
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/linear_regression.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb b/2023.2/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb b/2023.2/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb b/2023.2/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/ridge_regression.ipynb b/2023.2/.doctrees/nbsphinx/samples/ridge_regression.ipynb
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/ridge_regression.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/svc_adult.ipynb b/2023.2/.doctrees/nbsphinx/samples/svc_adult.ipynb
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/svc_adult.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/.doctrees/nbsphinx/samples/tsne.ipynb b/2023.2/.doctrees/nbsphinx/samples/tsne.ipynb
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2023.2/.doctrees/nbsphinx/samples/tsne.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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new file mode 100644
index 0000000000..e69de29bb2
diff --git a/2023.2/404.html b/2023.2/404.html
index 8395fa5de2..f1296f725f 100644
--- a/2023.2/404.html
+++ b/2023.2/404.html
@@ -1,28 +1,32 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Page not found &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="/scikit-learn-intelex/_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="/scikit-learn-intelex/_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="/scikit-learn-intelex/_static/custom.css" type="text/css" />
-      <link rel="stylesheet" href="/scikit-learn-intelex/_static/tabs.css" type="text/css" />
-    <link rel="shortcut icon" href="/scikit-learn-intelex/_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="/scikit-learn-intelex/_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+      <link rel="stylesheet" type="text/css" href="/scikit-learn-intelex/_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="/scikit-learn-intelex/_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="/scikit-learn-intelex/_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="/scikit-learn-intelex/_static/tabs.css" />
+
   
-        <script data-url_root="#" id="documentation_options" src="/scikit-learn-intelex/_static/documentation_options.js"></script>
-        <script src="/scikit-learn-intelex/_static/doctools.js"></script>
-        <script src="/scikit-learn-intelex/_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <link rel="shortcut icon" href="/scikit-learn-intelex/_static/favicons.png"/>
+      <script src="/scikit-learn-intelex/_static/jquery.js"></script>
+      <script src="/scikit-learn-intelex/_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="#" id="documentation_options" src="/scikit-learn-intelex/_static/documentation_options.js"></script>
+      <script src="/scikit-learn-intelex/_static/doctools.js"></script>
+      <script src="/scikit-learn-intelex/_static/sphinx_highlight.js"></script>
+      <script src="/scikit-learn-intelex/_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="/scikit-learn-intelex/_static/js/theme.js"></script>
     <link rel="index" title="Index" href="/scikit-learn-intelex/genindex.html" />
     <link rel="search" title="Search" href="/scikit-learn-intelex/search.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -48,11 +52,8 @@
           
           <a href="/scikit-learn-intelex/index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="/scikit-learn-intelex/_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="/scikit-learn-intelex/search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -61,52 +62,60 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/contribute.html">How to Contribute</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/license.html">License</a></li>
-</ul>
+
 
         </div>
       </div>
@@ -151,19 +160,7 @@ <h1>Page not found<a class="headerlink" href="#page-not-found" title="Permalink
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b09af2e0>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
-</a>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
-</a>
-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -176,6 +173,28 @@ <h1>Page not found<a class="headerlink" href="#page-not-found" title="Permalink
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/_sources/404.rst b/2023.2/_sources/404.rst
new file mode 100644
index 0000000000..3060a3f9cc
--- /dev/null
+++ b/2023.2/_sources/404.rst
@@ -0,0 +1,28 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+:orphan:
+:nosearch:
+
+Page not found
+==============
+
+Unfortunately, we could not find the page you were looking for. Try:
+
+- using :ref:`search <search>` to browse documentation
+- checking the table of contents on the left
+- filing an `issue <https://github.com/intel/scikit-learn-intelex/issues>`_ or
+  starting a `discussion <https://github.com/intel/scikit-learn-intelex/discussions>`_ on GitHub
\ No newline at end of file
diff --git a/2023.2/_sources/acceleration.rst b/2023.2/_sources/acceleration.rst
new file mode 100644
index 0000000000..25ad05049d
--- /dev/null
+++ b/2023.2/_sources/acceleration.rst
@@ -0,0 +1,47 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+##############
+Acceleration
+##############
+
+The software acceleration provided by |intelex| is achieved through the use of vector instructions,
+IA hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel platforms at launch time.
+
+|intelex| dynamically patches scikit-learn estimators to use Intel(R) oneAPI Data Analytics Library
+as the underlying solver, while getting the same solution faster.
+
+|intelex| depends on daal4py. You can learn more in `daal4py documentation <https://intelpython.github.io/daal4py>`_.
+
+Speedup over original Scikit-learn
+----------------------------------
+
+.. image:: _static/scikit-learn-acceleration-2021.2.3.PNG
+  :width: 800
+
+Configurations:
+
+- HW: c5.24xlarge AWS EC2 Instance using an Intel Xeon Platinum 8275CL with 2 sockets and 24 cores per socket
+- SW: scikit-learn version 0.24.2, scikit-learn-intelex version 2021.2.3, Python 3.8
+
+Kaggle Kernels
+**************
+
+Check out `Introduction to scikit-learn-intelex
+<https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook>`_,
+a Kaggle notebook that summarizes the speedup you can achieve with |intelex|.
+
+The acceleration is measured for a variety of machine learning workflows and Kaggle datasets.
\ No newline at end of file
diff --git a/2023.2/_sources/acceleration.rst.txt b/2023.2/_sources/acceleration.rst.txt
index b5c4884193..25ad05049d 100644
--- a/2023.2/_sources/acceleration.rst.txt
+++ b/2023.2/_sources/acceleration.rst.txt
@@ -37,8 +37,6 @@ Configurations:
 - HW: c5.24xlarge AWS EC2 Instance using an Intel Xeon Platinum 8275CL with 2 sockets and 24 cores per socket
 - SW: scikit-learn version 0.24.2, scikit-learn-intelex version 2021.2.3, Python 3.8
 
-.. Tip:: You can also try out the benchmarks used to create this data by going to Intel’s `scikit-learn_bench <https://github.com/IntelPython/scikit-learn_bench>`_ repository and following the relevant instructions.
-
 Kaggle Kernels
 **************
 
diff --git a/2023.2/_sources/algorithms.rst b/2023.2/_sources/algorithms.rst
new file mode 100644
index 0000000000..73397f31c3
--- /dev/null
+++ b/2023.2/_sources/algorithms.rst
@@ -0,0 +1,373 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _sklearn_algorithms:
+
+####################
+Supported Algorithms
+####################
+
+Applying |intelex| impacts the following scikit-learn algorithms:
+
+on CPU
+------
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVC`
+     - All parameters are supported
+     - No limitations
+   * - `NuSVC`
+     - All parameters are supported
+     - No limitations
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``cpp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - 
+       - For ``algorithm`` == `'kd_tree'`:
+       
+         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - For ``algorithm`` == `'brute'`:
+         
+         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Multi-output and sparse data are not supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` not in [`'lbfgs'`, `'newton-cg'`]
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVR`
+     - All parameters are supported
+     - No limitations
+   * - `NuSVR`
+     - All parameters are supported
+     - No limitations
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``cpp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Multi-output and sparse data are not supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported, `#observations` should be >= `#features`.
+   * - `Ridge`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``solver`` != `'auto'`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported, `#observations` should be >= `#features`.
+   * - `ElasticNet`
+     - All parameters are supported except:
+
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.
+   * - `Lasso`
+     - All parameters are supported except:
+
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+     - No limitations
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+
+       - ``svd_solver`` != `'full'`
+     - Sparse data is not supported
+   * - `TSNE`
+     - All parameters are supported except:
+
+       - ``metric`` != 'euclidean' or `'minkowski'` with ``p`` != `2`
+
+       Refer to :ref:`TSNE acceleration details <acceleration_tsne>` to learn more.
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - 
+       - For ``algorithm`` == 'kd_tree':
+         
+         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - For ``algorithm`` == 'brute':
+         
+         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Sparse data is not supported
+
+Other tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `train_test_split`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `assert_all_finite`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `pairwise_distance`
+     - All parameters are supported except:
+     
+       - ``metric`` not in [`'cosine'`, `'correlation'`]
+     - Only dense data is supported
+   * - `roc_auc_score`
+     - All parameters are supported except:
+       
+       - ``average`` != `None`
+       - ``sample_weight`` != `None`
+       - ``max_fpr`` != `None`
+       - ``multi_class`` != `None`
+     - No limitations
+
+on GPU
+------
+
+.. seealso:: :ref:`oneapi_gpu`
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVC`
+     - All parameters are supported except:
+
+       - ``kernel`` = `'sigmoid_poly'`
+       - ``class_weight`` != `None`
+     - Only binary dense data is supported
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``cpp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` != `'newton-cg'`
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+       - ``penalty`` != `'l2'`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``cpp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Only dense data is supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported, `#observations` should be >= `#features`.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+       
+       ``Init`` = `'k-means++'` fallbacks to CPU.
+     - Sparse data is not supported
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+     
+       - ``svd_solver`` != `'full'`
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+
+Scikit-learn tests
+------------------
+
+Monkey-patched scikit-learn classes and functions passes scikit-learn's own test
+suite, with few exceptions, specified in `deselected_tests.yaml
+<https://github.com/intel/scikit-learn-intelex/blob/master/deselected_tests.yaml>`__.
+
+The results of the entire latest scikit-learn test suite with |intelex|: `CircleCI
+<https://circleci.com/gh/intel/scikit-learn-intelex>`_.
\ No newline at end of file
diff --git a/2023.2/_sources/blogs.rst b/2023.2/_sources/blogs.rst
new file mode 100644
index 0000000000..2a264a8920
--- /dev/null
+++ b/2023.2/_sources/blogs.rst
@@ -0,0 +1,34 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _blogs:
+
+Follow us on Medium
+--------------------
+We publish blogs on Medium, so `follow us <https://medium.com/intel-analytics-software/tagged/machine-learning>`_
+to learn tips and tricks for more efficient data analysis the help of |intelex|.
+Here are our latest blogs:
+
+- `Save Time and Money with Intel Extension for Scikit-learn <https://medium.com/intel-analytics-software/save-time-and-money-with-intel-extension-for-scikit-learn-33627425ae4>`_,
+- `Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors <https://medium.com/intel-analytics-software/superior-machine-learning-performance-on-the-latest-intel-xeon-scalable-processor-efdec279f5a3>`_,
+- `Leverage Intel Optimizations in Scikit-Learn <https://medium.com/intel-analytics-software/leverage-intel-optimizations-in-scikit-learn-f562cb9d5544>`_,
+- `Intel Gives Scikit-Learn the Performance Boost Data Scientists Need <https://medium.com/intel-analytics-software/intel-gives-scikit-learn-the-performance-boost-data-scientists-need-42eb47c80b18>`_,
+- `From Hours to Minutes: 600x Faster SVM <https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae>`_,
+- `Improve the Performance of XGBoost and LightGBM Inference <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_,
+- `Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit <https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a>`_,
+- `Accelerate Your scikit-learn Applications <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_,
+- `Accelerate Linear Models for Machine Learning <https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe>`_,
+- `Accelerate K-Means Clustering <https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1>`_.
diff --git a/2023.2/_sources/blogs.rst.txt b/2023.2/_sources/blogs.rst.txt
index 2d9b0024e5..2a264a8920 100644
--- a/2023.2/_sources/blogs.rst.txt
+++ b/2023.2/_sources/blogs.rst.txt
@@ -32,4 +32,3 @@ Here are our latest blogs:
 - `Accelerate Your scikit-learn Applications <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_,
 - `Accelerate Linear Models for Machine Learning <https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe>`_,
 - `Accelerate K-Means Clustering <https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1>`_.
-- `Why Pay More for Machine Learning? <https://medium.com/intel-analytics-software/why-pay-more-for-machine-learning-893683bd78e4>`_.
diff --git a/2023.2/_sources/contribute.rst b/2023.2/_sources/contribute.rst
new file mode 100644
index 0000000000..b72c515b06
--- /dev/null
+++ b/2023.2/_sources/contribute.rst
@@ -0,0 +1,53 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+How to Contribute
+-----------------
+
+We welcome community contributions to |intelex|. You can:
+
+- Submit your changes directly with a `pull request <https://github.com/intel/scikit-learn-intelex/pulls>`_.
+- Log a bug or make a feature request with an `issue <https://github.com/intel/scikit-learn-intelex/issues>`_.
+
+Refer to our guidelines on pull requests and issues before you proceed.
+
+Issues
+******
+
+Use `GitHub issues <https://github.com/intel/scikit-learn-intelex/issues>`_ to:
+
+- report an issue
+- make a feature request
+
+.. note::
+   To report a vulnerability, refer to
+   `Intel vulnerability reporting policy <https://www.intel.com/content/www/us/en/security-center/default.html>`_.
+
+Pull Requests
+*************
+
+To contribute your changes directly to the repository, do the following:
+
+- Make sure you can build the product and run all the examples with your patch.
+- For a larger feature, provide a relevant example.
+- Document your code.
+- `Submit <https://github.com/intel/scikit-learn-intelex/pulls>`_ a pull request into the `master` branch.
+  Provide a brief description of the changes you are contributing.
+
+Public CI is enabled for the repository. Your PR should pass all of our checks.
+We will review your contribution and, if any additional fixes or modifications are necessary,
+we may give some feedback to guide you.
+When accepted, your pull request will be merged into our GitHub repository.
diff --git a/2023.2/_sources/distributed-mode.rst b/2023.2/_sources/distributed-mode.rst
new file mode 100644
index 0000000000..79dd663a3a
--- /dev/null
+++ b/2023.2/_sources/distributed-mode.rst
@@ -0,0 +1,30 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _distributed:
+
+Distributed Mode
+================
+
+.. note::
+
+   |intelex| contains scikit-learn patching functionality that was originally available in 
+   `daal4py <https://github.com/intel/scikit-learn-intelex/tree/master/daal4py>`_ package.
+   We recommend you to use scikit-learn-intelex package instead of daal4py.
+   You can learn more about daal4py in `daal4py documentation <https://intelpython.github.io/daal4py>`_.
+
+While daal4py is available in `distribued mode <https://intelpython.github.io/daal4py/scaling.html>`_,
+|intelex| does not currently offer this functionality.
diff --git a/latest/_sources/global-patching.rst.txt b/2023.2/_sources/global-patching.rst
similarity index 100%
rename from latest/_sources/global-patching.rst.txt
rename to 2023.2/_sources/global-patching.rst
diff --git a/2023.2/_sources/guide/acceleration.rst b/2023.2/_sources/guide/acceleration.rst
new file mode 100644
index 0000000000..388d192bbc
--- /dev/null
+++ b/2023.2/_sources/guide/acceleration.rst
@@ -0,0 +1,86 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+####################
+Acceleration Details
+####################
+
+The performance of some algorithms changes based on the parameters that are used.
+This section denotes the details of such cases.
+
+Refer to :ref:`sklearn_algorithms` to see the full list of algorithms, parameters, and data formats supported in |intelex|.
+
+.. _acceleration_tsne:
+
+TSNE
+----
+
+TSNE algorithm consists of two components: KNN and Gradient Descent.
+The overall acceleration of TSNE depends on the acceleration of each of these algorithms.
+
+- The KNN part of the algorithm supports all parameters except:
+
+  - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+- The Gradient Descent part of the algorithm supports all parameters except:
+
+  - ``n_components`` = `3`
+  - ``method`` = `'exact'`
+  - ``verbose`` != `0`
+
+To get better performance, use parameters supported by both components.
+
+.. _acceleration_rf:
+
+Random Forest
+-------------
+
+Random Forest models accelerated with |intelex| and using the `hist` splitting
+method discretize training data by creating a histogram with a configurable
+number of bins. The following keyword arguments can be used to influence the
+created histogram.
+
+.. list-table::
+   :widths: 10 10 10 30
+   :header-rows: 1
+   :align: left
+
+   * - Keyword argument
+     - Possible values
+     - Default value
+     - Description
+   * - ``maxBins``
+     - `[0, inf)`
+     - ``256``
+     - Number of bins in the histogram with the discretized training data. The
+       value ``0`` disables data discretization.
+   * - ``minBinSize``
+     - `[1, inf)`
+     - ``5``
+     - Minimum number of training data points in each bin after discretization.
+   * - ``binningStrategy``
+     - ``quantiles, averages``
+     - ``quantiles``
+     - Selects the algorithm used to calculate bin edges. ``quantiles``
+       results in bins with a similar amount of training data points. ``averages``
+       divides the range of values observed in the training data set into
+       equal-width bins of size `(max - min) / maxBins`.
+
+Note that using discretized training data can greatly accelerate model training
+times, especially for larger data sets. However, due to the reduced fidelity of
+the data, the resulting model can present worse performance metrics compared to
+a model trained on the original data. In such cases, the number of bins can be
+increased with the ``maxBins`` parameter, or binning can be disabled entirely by
+setting ``maxBins=0``.
\ No newline at end of file
diff --git a/2023.2/_sources/guide/acceleration.rst.txt b/2023.2/_sources/guide/acceleration.rst.txt
index 2654a548a6..388d192bbc 100644
--- a/2023.2/_sources/guide/acceleration.rst.txt
+++ b/2023.2/_sources/guide/acceleration.rst.txt
@@ -14,9 +14,9 @@
 .. * limitations under the License.
 .. *******************************************************************************/
 
-########################
-Performance Tuning Guide
-########################
+####################
+Acceleration Details
+####################
 
 The performance of some algorithms changes based on the parameters that are used.
 This section denotes the details of such cases.
@@ -83,4 +83,4 @@ times, especially for larger data sets. However, due to the reduced fidelity of
 the data, the resulting model can present worse performance metrics compared to
 a model trained on the original data. In such cases, the number of bins can be
 increased with the ``maxBins`` parameter, or binning can be disabled entirely by
-setting ``maxBins=0``.
+setting ``maxBins=0``.
\ No newline at end of file
diff --git a/2023.2/_sources/index.rst b/2023.2/_sources/index.rst
new file mode 100644
index 0000000000..f7389c77f0
--- /dev/null
+++ b/2023.2/_sources/index.rst
@@ -0,0 +1,86 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |intelex_repo| replace:: |intelex| repository
+.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
+
+.. _index:
+
+#########
+|intelex|
+#########
+
+With |intelex| you can accelerate your Scikit-learn applications and
+still have full conformance with all Scikit-Learn APIs and algorithms.
+|intelex| is a free software AI accelerator that brings over 10-100X acceleration across a variety of applications.
+
+|intelex| offers you a way to accelerate existing scikit-learn code.
+The acceleration is achieved through :term:`patching`:
+replacing the stock scikit-learn algorithms with their optimized versions provided by the extension.
+
+.. rubric:: Designed for Data Scientists and Framework Designers
+
+|intelex| was created to provide data scientists with a way to get a better performance
+while using the familiar scikit-learn package and getting the same results.
+
+Usage
+------
+
+.. include:: usage.rst
+
+Important Links
+--------------------
+- |intelex_repo|_
+- `Machine Learning Benchmarks <https://github.com/IntelPython/scikit-learn_bench>`_
+- `About Intel(R) oneAPI Data Analytics Library <https://github.com/oneapi-src/oneDAL>`_
+- `About daal4py <https://github.com/intel/scikit-learn-intelex/tree/master/daal4py>`_
+
+.. toctree::
+   :caption: About
+   :hidden:
+
+   acceleration.rst
+   what-is-patching.rst
+   Medium Blogs <blogs.rst>
+   System Requirements <system-requirements.rst>
+   memory-requirements.rst
+
+.. toctree::
+   :caption: Get Started
+   :hidden:
+
+   installation.rst
+   quick-start.rst
+   samples.rst
+   kaggle.rst
+
+.. toctree::
+   :caption: Developer Guide
+   :hidden:
+
+   algorithms.rst
+   guide/acceleration.rst
+   global-patching.rst
+   verbose.rst
+   distributed-mode.rst
+   oneAPI and GPU support <oneapi-gpu.rst>
+
+.. toctree::
+   :caption: Community
+   :hidden:
+
+   support.rst
+   contribute.rst
diff --git a/2023.2/_sources/index.rst.txt b/2023.2/_sources/index.rst.txt
index 5422ba696e..f7389c77f0 100644
--- a/2023.2/_sources/index.rst.txt
+++ b/2023.2/_sources/index.rst.txt
@@ -49,75 +49,38 @@ Important Links
 - `About daal4py <https://github.com/intel/scikit-learn-intelex/tree/master/daal4py>`_
 
 .. toctree::
-   :caption: Get Started
-   :titlesonly: 
+   :caption: About
    :hidden:
-   :maxdepth: 1
 
-   quick-start.rst
+   acceleration.rst
    what-is-patching.rst
-   support.rst
-   
-.. toctree::
-   :caption: Features
-   :hidden:
-   :maxdepth: 2
-
-   algorithms.rst
-   oneAPI and GPU support <oneapi-gpu.rst>
-   distributed-mode.rst
-   verbose.rst
-
-.. toctree::
-   :caption: Releases
-   :hidden:
-   :maxdepth: 2
-
-   releases.rst
-   deprecation.rst
-
-.. toctree::
-   :caption: Installation Guide
-   :hidden:
-   :maxdepth: 2
-
-   installation.rst
+   Medium Blogs <blogs.rst>
    System Requirements <system-requirements.rst>
    memory-requirements.rst
 
 .. toctree::
-   :caption: Examples
+   :caption: Get Started
    :hidden:
-   :maxdepth: 2
 
+   installation.rst
+   quick-start.rst
    samples.rst
    kaggle.rst
 
 .. toctree::
-   :caption: Performance
+   :caption: Developer Guide
    :hidden:
-   :maxdepth: 2
 
-   acceleration.rst
+   algorithms.rst
    guide/acceleration.rst
+   global-patching.rst
+   verbose.rst
+   distributed-mode.rst
+   oneAPI and GPU support <oneapi-gpu.rst>
 
 .. toctree::
-   :caption: Blogs & Publications
-   :hidden:
-   :maxdepth: 2
-
-   Medium Blogs <blogs.rst>
-
-.. toctree::
-   :caption: How to contribute
+   :caption: Community
    :hidden:
-   :maxdepth: 2
 
+   support.rst
    contribute.rst
-
-.. toctree::
-   :caption: License
-   :hidden:
-   :maxdepth: 2
-
-   license.rst
diff --git a/2023.2/_sources/installation.rst b/2023.2/_sources/installation.rst
new file mode 100644
index 0000000000..f082de2ac4
--- /dev/null
+++ b/2023.2/_sources/installation.rst
@@ -0,0 +1,187 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |intelex_repo| replace:: |intelex| repository
+.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
+
+#############
+Installation
+#############
+
+|intelex| is available at the `Python Package Index <https://pypi.org/project/scikit-learn-intelex/>`_,
+on Anaconda Cloud in `Conda-Forge channel <https://anaconda.org/conda-forge/scikit-learn-intelex>`_ and
+in `Intel channel <https://anaconda.org/intel/scikit-learn-intelex>`_.
+
+|intelex| is also available as a part of `Intel AI Analytics Toolkit
+<https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html>`_ (AI Kit).
+If you already have AI Kit installed, you do not need to separately install the extension.
+
+You can also build the extension from :ref:`sources <build_from_sources>`.
+
+.. seealso:: Check out :ref:`system_requirements` before you start the installation process.
+
+Install from distribution channels
+-----------------------------------
+
+.. contents:: :local:
+
+Install from PyPI (recommended by default)
+===========================================
+
+#. **[Optional step] [Recommended]** To prevent version conflicts, create and activate a new environment:
+
+   .. tabs::
+
+      .. tab:: Linux
+
+         ::
+
+            python -m venv env
+            source env/bin/activate
+
+      .. tab:: Windows
+
+         ::
+
+            python -m venv env
+            .\env\Scripts\activate
+
+#. Install `scikit-learn-intelex`:
+
+   ::
+
+      pip install scikit-learn-intelex
+
+Install from Anaconda Cloud
+============================
+
+To prevent version conflicts, we recommend installing `scikit-learn-intelex` into a new conda environment.
+For each distribution channel, there are two installation options: with and without a creation of a new environment.
+
+- Install from **Anaconda Cloud: Conda-Forge channel** (recommended by default for conda users):
+
+  - into a newly created environment (recommended)::
+
+       conda create -n env -c conda-forge python=3.9 scikit-learn-intelex
+
+    .. important::
+
+       If you do not specify the version of Python (``python=3.9`` in the example above), then Python 3.10 is downloaded by default,
+       which is not supported.
+
+       See :ref:`supported configurations for Conda-Forge channel <sys_req_conda_forge>`.
+
+  - into your current environment::
+
+       conda install scikit-learn-intelex -c conda-forge
+
+- Install from **Anaconda Cloud: Intel channel** (recommended for the users of Intel® Distribution for Python):
+
+  - into a newly created environment (recommended)::
+
+       conda create -n env -c intel python scikit-learn-intelex
+
+    Note that you may also specify the version of Python to download
+    (see :ref:`supported configurations for Anaconda Intel channel <sys_req_conda_intel>`).
+    For example::
+
+       conda create -n env -c intel python=3.7 scikit-learn-intelex
+
+  - into your current environment::
+
+       conda install scikit-learn-intelex -c intel
+
+- Install from **Anaconda Cloud: Main channel**:
+
+  - into a newly created environment (recommended)::
+
+       conda create -n env python=3.9 scikit-learn-intelex
+
+    .. important::
+
+       If you do not specify the version of Python (``python=3.9`` in the example above), then Python 3.10 is downloaded by default,
+       which is not supported.
+
+       See :ref:`supported configurations for Anaconda main channel <sys_req_conda_main>`.
+
+  - into your current environment::
+
+       conda install scikit-learn-intelex
+
+.. _build_from_sources:
+
+Build from Sources
+---------------------
+
+Sources are available in |intelex_repo|_.
+
+.. rubric:: Prerequisites
+
+::
+
+    Python version >= 3.6, < 3.10
+    daal4py >= 2021.4
+
+.. note::
+    You can `build daal4py from sources <https://github.com/intel/scikit-learn-intelex/blob/master/daal4py/INSTALL.md>`_ or get it from `distribution channels
+    <https://intelpython.github.io/daal4py/#getting-daal4py>`_.
+
+Configure the build with environment variables
+==============================================
+
+::
+
+    SKLEARNEX_VERSION: sets package version
+
+Build |intelex|
+===============
+
+To install the package::
+
+    cd <checkout-dir>
+    python setup_sklearnex.py install
+
+To install the package in the development mode::
+
+    cd <checkout-dir>
+    python setup.py develop
+
+To install scikit-learn-intelex without downloading daal4py::
+
+    cd <checkout-dir>
+    python setup_sklearnex.py install --single-version-externally-managed --record=record.txt
+
+To install scikit-learn-intelex without downloading daal4py in the development mode::
+
+    cd <checkout-dir>
+    python setup_sklearnex.py develop --no-deps
+
+.. note::
+    The ``develop`` mode will not install the package but it will create a ``.egg-link`` in the deployment directory
+    back to the project source code directory. That way you can edit the source code and see the changes
+    without having to reinstall the package every time you make a small change.
+
+⚠️ Keys ``--single-version-externally-managed`` and ``--no-deps`` are required so that daal4py is not downloaded after installation of |intelex|
+
+.. note::
+    ``--single-version-externally-managed`` is an option used for Python packages instructing the setuptools module
+    to create a Python package that can be easily managed by the package manager on the host
+
+Next Steps
+==========
+
+- :ref:`What is patching and how to patch scikit-learn <what_is_patching>`
+- :ref:`How to use scikit-learn-intelex <get_started>`
diff --git a/2023.2/_sources/installation.rst.txt b/2023.2/_sources/installation.rst.txt
index be10e818bc..f082de2ac4 100644
--- a/2023.2/_sources/installation.rst.txt
+++ b/2023.2/_sources/installation.rst.txt
@@ -184,3 +184,4 @@ Next Steps
 ==========
 
 - :ref:`What is patching and how to patch scikit-learn <what_is_patching>`
+- :ref:`How to use scikit-learn-intelex <get_started>`
diff --git a/2023.2/_sources/kaggle.rst b/2023.2/_sources/kaggle.rst
new file mode 100644
index 0000000000..a00152a403
--- /dev/null
+++ b/2023.2/_sources/kaggle.rst
@@ -0,0 +1,44 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _kaggle:
+
+Kaggle Kernels
+--------------------
+
+Check out Kaggle notebooks created by |intelex| developers.
+
+.. rubric:: Acceleration
+
+`Introduction to scikit-learn-intelex <https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook>`_
+provides a summary of the speedup you can achieve with |intelex|.
+
+.. rubric:: Machine Learning Workflows
+
+Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.
+
+Kaggle kernels that use scikit-learn and |intelex|:
+
+.. toctree::
+
+   Classification Tasks <kaggle/classification.rst>
+   Regression Tasks <kaggle/regression.rst>
+
+Kaggle kernels that use AutoML with |intelex|:
+
+.. toctree::
+
+   AutoML Workflows <kaggle/automl.rst>
diff --git a/2023.2/_sources/kaggle/automl.rst b/2023.2/_sources/kaggle/automl.rst
new file mode 100644
index 0000000000..0156e5ec89
--- /dev/null
+++ b/2023.2/_sources/kaggle/automl.rst
@@ -0,0 +1,66 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |automl_with_intelex_jun| replace:: AutoML MultiClass Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with |intelex| 
+.. _automl_with_intelex_jun: https://www.kaggle.com/alex97andreev/tps-jun-autogluon-with-sklearnex
+
+.. |automl_with_intelex_tps_oct| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest) using AutoGluon with |intelex|
+.. _automl_with_intelex_tps_oct: https://www.kaggle.com/lordozvlad/fast-automl-with-intel-extension-for-scikit-learn/notebook
+
+.. |automl_with_intelex_tps_nov| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using EvalML and AutoGluon with |intelex|
+.. _automl_with_intelex_tps_nov: https://www.kaggle.com/lordozvlad/tps-nov-automl-with-intel-extension
+
+.. |automl_with_intelex_titanic| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with |intelex|
+.. _automl_with_intelex_titanic: https://www.kaggle.com/lordozvlad/titanic-automl-with-intel-extension-for-sklearn/notebook
+
+.. |automl_with_intelex_tps_jan| replace:: AutoML Binary Classification (Random Forest, SVR, Blending) using PyCaret with |intelex|
+.. _automl_with_intelex_tps_jan: https://www.kaggle.com/code/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex/notebook
+
+
+Kaggle Kernels that use AutoML and |intelex|
+--------------------------------------------
+
+The following Kaggle kernels show how to patch autoML frameworks with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 40 20
+
+   * - Kernel
+     - Goal
+   * - |automl_with_intelex_jun|_
+
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+   * - |automl_with_intelex_titanic|_
+
+       **Data:** Titanic datset
+     - Predict whether a passenger survivies
+   * - |automl_with_intelex_tps_oct|_
+
+       **Data:** [TPS Oct 2021] Synthetic molecular response data
+     - Predict the biological response of molecules given various chemical properties
+   * - |automl_with_intelex_tps_nov|_
+
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+     - Identify spam emails via features extracted from the email
+   * - |automl_with_intelex_tps_jan|_
+
+       **Data:** [TPS Jan 2022] Fictional Sales data
+     - Predict the corresponding item sales for each date-country-store-item combination
\ No newline at end of file
diff --git a/2023.2/_sources/kaggle/classification.rst b/2023.2/_sources/kaggle/classification.rst
new file mode 100644
index 0000000000..5d7b94345f
--- /dev/null
+++ b/2023.2/_sources/kaggle/classification.rst
@@ -0,0 +1,244 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Kaggle Kernels for Classification Tasks
+***************************************
+
+The following Kaggle kernels show how to patch scikit-learn with |intelex| for various classification tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+Binary Classification
++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Logistic Regression for Binary Classification
+       <https://www.kaggle.com/alexeykolobyanin/tps-nov-log-regression-with-sklearnex-17x-speedup>`_
+     
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+
+     - Identify spam emails via features extracted from the email
+     - 
+
+       - data preprocessing (normalization)
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Feature Importance in Random Forest for Binary Classification
+       <https://www.kaggle.com/code/lordozvlad/fast-feature-importance-using-scikit-learn-intelex/notebook>`_
+     
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+
+     - Identify spam emails via features extracted from the email
+     - 
+      
+       - reducing DataFrame memory usage
+       - computing feature importance with ELI5 and the default scikit-learn permutation importance
+       - training using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest for Binary Classification
+       <https://www.kaggle.com/andreyrus/tps-apr-rf-with-intel-extension-for-scikit-learn>`_
+     
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+      
+       - data preprocessing
+       - feature construction
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for Binary Classification
+       <https://www.kaggle.com/napetrov/tps04-svm-with-intel-extension-for-scikit-learn>`_
+       
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+     
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) with Feature Preprocessing for Binary Classification
+       <https://www.kaggle.com/napetrov/tps04-svm-with-scikit-learn-intelex>`_
+      
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+     
+       - data preprocessing
+       - feature engineering
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+MultiClass Classification
++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Logistic Regression for MultiClass Classification with Quantile Transformer
+       <https://www.kaggle.com/kppetrov/tps-jun-fast-logreg-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+
+       - data preprocessing with Quantile Transformer
+       - training and prediction using scikit-learn-intelex
+       - search for optimal paramters using Optuna
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for MultiClass Classification
+       <https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS May 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+
+   * - `Stacking Classifer with Logistic Regression, kNN, Random Forest, and Quantile Transformer
+       <https://www.kaggle.com/owerbat/tps-jun-fast-stacking-with-scikit-learn-intelex>`_
+      
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+
+       - data preprocessing: one-hot encoding, dimensionality reduction with PCA, normalization
+       - creating a stacking classifier with logistic regression, kNN, and random forest,
+         and a pipeline of Quantile Transformer and another logistic regression as a final estimator
+       - searching for optimal parameters for the stacking classifier
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for MultiClass Classification
+       <https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup>`_
+       
+       **Data:** [TPS Dec 2021] Synthetic Forest Cover Type data
+     - Predict the forest cover type
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Feature Importance in Random Forest for MultiClass Classification
+       <https://www.kaggle.com/code/lordozvlad/tps-dec-fast-feature-importance-with-sklearnex>`_
+     
+       **Data:** [TPS Dec 2021] Synthetic Forest Cover Type data
+
+     - Predict the forest cover type
+     - 
+      
+       - reducing DataFrame memory usage
+       - computing feature importance with ELI5
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `k-Nearest Neighbors (kNN) for MultiClass Classification
+       <https://www.kaggle.com/code/alexeykolobyanin/tps-feb-knn-with-sklearnex-13x-speedup>`_
+       
+       **Data:** [TPS Feb 2022] Bacteria DNA
+     - Predict bacteria species based on repeated lossy measurements of DNA snippets
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Classification Tasks in Computer Vision
++++++++++++++++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Support Vector Classification (SVC) for MultiClass Classification (CV task)
+       <https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-mnist?scriptVersionId=58739300>`_
+       
+       **Data:** Digit Recognizer (MNIST)
+     - Recognize hand-written digits
+     - 
+       
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `k-Nearest Neighbors (kNN) for MultiClass Classification (CV task)
+       <https://www.kaggle.com/kppetrov/fast-knn-using-scikit-learn-intelex-for-mnist?scriptVersionId=58738635>`_
+       
+       **Data:** Digit Recognizer (MNIST)
+     - Recognize hand-written digits
+     - 
+     
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Classification Tasks in Natural Language Processing
++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Support Vector Classification (SVC) for a Binary Classification (NLP task)
+       <https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-nlp?scriptVersionId=58739339>`_
+       
+       **Data:** Natural Language Processing with Disaster Tweets
+     - Predict which tweets are about real disasters and which ones are not
+     - 
+     
+       - data preprocessing
+       - TF-IDF calculation
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `One-vs-Rest Support Vector Machine (SVM) with Text Data for MultiClass Classification
+       <https://www.kaggle.com/kppetrov/using-scikit-learn-intelex-for-what-s-cooking?scriptVersionId=58739642>`_
+      
+       **Data:** What's Cooking
+     - Use recipe ingredients to predict the cuisine
+     - 
+     
+       - feature extraction using TfidfVectorizer
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for Binary Classification with Sparse Data (NLP task)
+       <https://www.kaggle.com/code/alex97andreev/fast-svm-for-sparse-data-from-nlp-problem>`_
+       
+       **Data:** Stack Overflow questions
+     - Predict the binary quality rating for Stack Overflow questions
+     - 
+     
+       - data preprocessing
+       - TF-IDF calculation
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
diff --git a/2023.2/_sources/kaggle/regression.rst b/2023.2/_sources/kaggle/regression.rst
new file mode 100644
index 0000000000..abdbebc15b
--- /dev/null
+++ b/2023.2/_sources/kaggle/regression.rst
@@ -0,0 +1,156 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Kaggle Kernels for Regression Tasks
+************************************
+
+The following Kaggle kernels show how to patch scikit-learn with |intelex| for various regression tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+Using a Single Regressor
+++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel
+       <https://www.kaggle.com/kppetrov/tps-jul-2021-baseline-with-nusvr-model/notebook>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - data preprocessing
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+   * - `Nu Support Vector Regression (nuSVR)
+       <https://www.kaggle.com/alexeykolobyanin/tps-aug-nusvr-with-intel-extension-for-sklearn>`__
+     
+       **Data:** [TPS Aug 2021] Synthetic loan data
+     - Calculate loss associated with a loan defaults
+     -
+
+       - data preprocessing
+       - feature engineering
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Nu Support Vector Regression (nuSVR)
+       <https://www.kaggle.com/alexeykolobyanin/house-prices-nusvr-sklearn-intelex-4x-speedup>`__
+       
+       **Data:** House Prices dataset
+     - Predict sale prices for a property based on its characteristics
+     -
+
+       - data preprocessing
+       - exploring outliers
+       - feature engineering
+       - filling missing values
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest Regression
+       <https://www.kaggle.com/pahandrovich/tps-jul-2021-fast-randomforest-with-sklearnex>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - checking correlation between features
+       - search for best paramters using GridSearchCV
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Random Forest Regression with Feature Engineering
+       <https://www.kaggle.com/alexeykolobyanin/tps-jul-rf-with-intel-extension-for-scikit-learn>`_
+     
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - data preprocessing
+       - feature engineering
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest Regression with Feature Importance Computation
+       <https://www.kaggle.com/code/lordozvlad/tps-mar-fast-workflow-using-scikit-learn-intelex>`_
+
+       **Data:** [TPS Mar 2022] Spatio-temporal traffic data
+     - Forecast twelve-hours of traffic flow in a major U.S. metropolitan area
+     -
+
+       - feature engineering
+       - computing feature importance with ELI5
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Ridge Regression
+       <https://www.kaggle.com/alexeykolobyanin/tps-sep-ridge-with-sklearn-intelex-2x-speedup>`_
+     
+     
+       **Data:** [TPS Sep 2021] Synthetic insurance data
+     - Predict the probability of a customer making a claim upon an insurance policy
+     -
+
+       - data preprocessing
+       - filling missing values
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Stacking Regressors
++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Stacking Regressor with Random Fores, SVR, and LASSO
+       <https://www.kaggle.com/alexeykolobyanin/tps-jul-stacking-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - feature engineering
+       - creating a stacking regressor
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data
+       <https://www.kaggle.com/alexeykolobyanin/predict-sales-stacking-with-scikit-learn-intelex>`_
+       
+       **Data:** Predict Future Sales dataset
+     - Predict total sales for every product and store in the next month based on daily sales data
+     -
+
+       - data preprocessing
+       - creating a stacking regressor
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
diff --git a/latest/_sources/memory-requirements.rst.txt b/2023.2/_sources/memory-requirements.rst
similarity index 100%
rename from latest/_sources/memory-requirements.rst.txt
rename to 2023.2/_sources/memory-requirements.rst
diff --git a/2023.2/_sources/oneapi-gpu.rst b/2023.2/_sources/oneapi-gpu.rst
new file mode 100644
index 0000000000..460ff2645d
--- /dev/null
+++ b/2023.2/_sources/oneapi-gpu.rst
@@ -0,0 +1,113 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _oneapi_gpu:
+
+##############################################################
+oneAPI and GPU support in |intelex|
+##############################################################
+
+|intelex| supports oneAPI concepts, which
+means that algorithms can be executed on different devices: CPUs and GPUs.
+This is done via integration with
+`dpctl <https://intelpython.github.io/dpctl/latest/index.html>`_ package that
+implements core oneAPI concepts like queues and devices.
+
+Prerequisites
+-------------
+
+For execution on GPU, DPC++ compiler runtime and driver are required. Refer to `DPC++ system
+requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_ for details.
+
+DPC++ compiler runtime can be installed either from PyPI or Anaconda:
+
+- Install from PyPI::
+
+     pip install dpcpp-cpp-rt
+
+- Install from Anaconda::
+
+     conda install dpcpp_cpp_rt -c intel
+
+Device offloading
+-----------------
+
+|intelex| offers two options for running an algorithm on a
+specific device with the help of dpctl:
+
+- Pass input data as `dpctl.tensor.usm_ndarray <https://intelpython.github.io/dpctl/latest/docfiles/dpctl/usm_ndarray.html#dpctl.tensor.usm_ndarray>`_ to the algorithm.
+
+  The computation will run on the device where the input data is
+  located, and the result will be returned as :code:`usm_ndarray` to the same
+  device.
+
+  .. note::
+    All the input data for an algorithm must reside on the same device.
+
+  .. warning::
+    The :code:`usm_ndarray` can only be consumed by the base methods
+    like :code:`fit`, :code:`predict`, and :code:`transform`.
+    Note that only the algorithms in |intelex| support
+    :code:`usm_ndarray`. The algorithms from the stock version of scikit-learn
+    do not support this feature.
+- Use global configurations of |intelex|\*:
+  
+  1. The :code:`target_offload` option can be used to set the device primarily
+     used to perform computations. Accepted data types are :code:`str` and
+     :code:`dpctl.SyclQueue`. If you pass a string to :code:`target_offload`,
+     it should either be ``"auto"``, which means that the execution
+     context is deduced from the location of input data, or a string
+     with SYCL* filter selector. The default value is ``"auto"``.
+  
+  2. The :code:`allow_fallback_to_host` option
+     is a Boolean flag. If set to :code:`True`, the computation is allowed 
+     to fallback to the host device when a particular estimator does not support
+     the selected device. The default value is :code:`False`.
+
+These options can be set using :code:`sklearnex.set_config()` function or
+:code:`sklearnex.config_context`. To obtain the current values of these options,
+call :code:`sklearnex.get_config()`.
+
+.. note::
+     Functions :code:`set_config`, :code:`get_config` and :code:`config_context`
+     are always patched after the :code:`sklearnex.patch_sklearn()` call.
+
+.. rubric:: Compatibility considerations
+
+For compatibility reasons, algorithms in |intelex| may be offloaded to the device using
+:code:`daal4py.oneapi.sycl_context`. However, it is recommended to use one of the options
+described above for device offloading instead of using :code:`sycl_context`.
+
+Example
+-------
+
+An example on how to patch your code with Intel CPU/GPU optimizations:
+
+.. code-block:: python
+
+   from sklearnex import patch_sklearn, config_context
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   with config_context(target_offload="gpu:0"):
+      clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+Note: Current offloading behavior restricts fitting and inference of any models to be
+in the same context or absence of context. For example, a model trained in the GPU context with
+target_offload="gpu:0" throws an error if the inference is made outside the same GPU context.
diff --git a/2023.2/_sources/oneapi-gpu.rst.txt b/2023.2/_sources/oneapi-gpu.rst.txt
index 76a2a752f4..460ff2645d 100644
--- a/2023.2/_sources/oneapi-gpu.rst.txt
+++ b/2023.2/_sources/oneapi-gpu.rst.txt
@@ -108,7 +108,6 @@ An example on how to patch your code with Intel CPU/GPU optimizations:
    with config_context(target_offload="gpu:0"):
       clustering = DBSCAN(eps=3, min_samples=2).fit(X)
 
-
-.. note:: Current offloading behavior restricts fitting and inference of any models to be
-     in the same context or absence of context. For example, a model trained in the GPU context with
-     target_offload="gpu:0" throws an error if the inference is made outside the same GPU context.
+Note: Current offloading behavior restricts fitting and inference of any models to be
+in the same context or absence of context. For example, a model trained in the GPU context with
+target_offload="gpu:0" throws an error if the inference is made outside the same GPU context.
diff --git a/2023.2/_sources/quick-start.rst b/2023.2/_sources/quick-start.rst
new file mode 100644
index 0000000000..40c0cd914c
--- /dev/null
+++ b/2023.2/_sources/quick-start.rst
@@ -0,0 +1,29 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _get_started:
+
+############################################
+Quick Start
+############################################
+
+.. include:: usage.rst
+
+.. seealso::
+
+   - :ref:`Supported algorithms and parameters <sklearn_algorithms>`
+   - :ref:`samples`
+   - :ref:`global_patching`
\ No newline at end of file
diff --git a/2023.2/_sources/quick-start.rst.txt b/2023.2/_sources/quick-start.rst.txt
index a7b000c7dc..40c0cd914c 100644
--- a/2023.2/_sources/quick-start.rst.txt
+++ b/2023.2/_sources/quick-start.rst.txt
@@ -14,7 +14,7 @@
 .. * limitations under the License.
 .. *******************************************************************************/
 
-.. _quick_start:
+.. _get_started:
 
 ############################################
 Quick Start
@@ -26,5 +26,4 @@ Quick Start
 
    - :ref:`Supported algorithms and parameters <sklearn_algorithms>`
    - :ref:`samples`
-   - :ref:`global_patching`
-   - `Getting Started Guide <https://www.intel.com/content/www/us/en/developer/articles/guide/intel-extension-for-scikit-learn-getting-started.html>`_
+   - :ref:`global_patching`
\ No newline at end of file
diff --git a/2023.2/_sources/samples.rst b/2023.2/_sources/samples.rst
new file mode 100644
index 0000000000..2e54af0363
--- /dev/null
+++ b/2023.2/_sources/samples.rst
@@ -0,0 +1,58 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _samples:
+
+#######
+Samples
+#######
+
+The following samples are also provided as Jupyter notebooks in |intelex| repository.
+If you want to run them locally, refer to `these instructions
+<https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks>`_.
+
+.. rubric:: Classification Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   k-Nearest Neighbors (kNN) for MNIST dataset <samples/knn_mnist.ipynb>
+   Logistic Regression for Cifar dataset <samples/logistictic_regression_cifar.ipynb>
+   Support Vector Classification (SVC) for Adult dataset <samples/svc_adult.ipynb>
+
+.. rubric:: Regression Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   ElasticNet for Airlines DepDelay dataset <samples/ElasticNet.ipynb>
+   Lasso Regression for YearPredictionMSD dataset <samples/lasso_regression.ipynb>
+   Linear Regression for YearPredictionMSD dataset <samples/linear_regression.ipynb>
+   Nu-Support Vector Regression (NuSVR) for Medical Charges dataset <samples/nusvr_medical_charges.ipynb>
+   Random Forest for Yolanda dataset <samples/random_forest_yolanda.ipynb>
+   Rigde Regression for Airlines DepDelay dataset <samples/ridge_regression.ipynb>
+
+.. rubric:: Clustering Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   Kmeans for spoken arabic digit dataset <samples/kmeans.ipynb>
+   DBSCAN for spoken arabic digit dataset <samples/dbscan.ipynb>
+
+.. seealso::
+
+   There are also :ref:`Kaggle kernels <kaggle>` that use |intelex| for a variety of machine learning scenarios.
\ No newline at end of file
diff --git a/2023.2/_sources/samples.rst.txt b/2023.2/_sources/samples.rst.txt
index 1e14a7880c..2e54af0363 100644
--- a/2023.2/_sources/samples.rst.txt
+++ b/2023.2/_sources/samples.rst.txt
@@ -53,16 +53,6 @@ If you want to run them locally, refer to `these instructions
    Kmeans for spoken arabic digit dataset <samples/kmeans.ipynb>
    DBSCAN for spoken arabic digit dataset <samples/dbscan.ipynb>
 
-.. rubric:: Jupyter Notebook Samples
-
-.. toctree::
-   :maxdepth: 1
-
-   Intel® Extension for Scikit-Learn* Getting Started Sample <https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted>
-   Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample <https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult>
-
 .. seealso::
 
-   There are also :ref:`Kaggle kernels <kaggle>` that use |intelex| for a variety of machine learning scenarios.
-
-.. note:: Explore the complete list of oneAPI code samples in the `oneAPI Samples Catalog <https://oneapi-src.github.io/oneAPI-samples/>`_. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.
\ No newline at end of file
+   There are also :ref:`Kaggle kernels <kaggle>` that use |intelex| for a variety of machine learning scenarios.
\ No newline at end of file
diff --git a/2023.2/_sources/samples/ElasticNet.ipynb b/2023.2/_sources/samples/ElasticNet.ipynb
new file mode 100644
index 0000000000..29e5dca851
--- /dev/null
+++ b/2023.2/_sources/samples/ElasticNet.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/ElasticNet.ipynb.txt b/2023.2/_sources/samples/ElasticNet.ipynb.txt
index 9ffdd8fe1c..29e5dca851 100644
--- a/2023.2/_sources/samples/ElasticNet.ipynb.txt
+++ b/2023.2/_sources/samples/ElasticNet.ipynb.txt
@@ -164,7 +164,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/daal4py_data_science.ipynb b/2023.2/_sources/samples/daal4py_data_science.ipynb
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2023.2/_sources/samples/daal4py_data_science.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2023.2/_sources/samples/dbscan.ipynb b/2023.2/_sources/samples/dbscan.ipynb
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2023.2/_sources/samples/dbscan.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/dbscan.ipynb.txt b/2023.2/_sources/samples/dbscan.ipynb.txt
index 829719629a..d6e5c92653 100644
--- a/2023.2/_sources/samples/dbscan.ipynb.txt
+++ b/2023.2/_sources/samples/dbscan.ipynb.txt
@@ -128,7 +128,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/kmeans.ipynb b/2023.2/_sources/samples/kmeans.ipynb
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2023.2/_sources/samples/kmeans.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/kmeans.ipynb.txt b/2023.2/_sources/samples/kmeans.ipynb.txt
index 8e6470f568..df09f8ded5 100644
--- a/2023.2/_sources/samples/kmeans.ipynb.txt
+++ b/2023.2/_sources/samples/kmeans.ipynb.txt
@@ -139,7 +139,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/knn_mnist.ipynb b/2023.2/_sources/samples/knn_mnist.ipynb
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2023.2/_sources/samples/knn_mnist.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/knn_mnist.ipynb.txt b/2023.2/_sources/samples/knn_mnist.ipynb.txt
index 6b7e2465a0..b8604d70f0 100644
--- a/2023.2/_sources/samples/knn_mnist.ipynb.txt
+++ b/2023.2/_sources/samples/knn_mnist.ipynb.txt
@@ -104,7 +104,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/lasso_regression.ipynb b/2023.2/_sources/samples/lasso_regression.ipynb
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2023.2/_sources/samples/lasso_regression.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/lasso_regression.ipynb.txt b/2023.2/_sources/samples/lasso_regression.ipynb.txt
index 77571c2432..967d0d4e54 100644
--- a/2023.2/_sources/samples/lasso_regression.ipynb.txt
+++ b/2023.2/_sources/samples/lasso_regression.ipynb.txt
@@ -162,7 +162,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/linear_regression.ipynb b/2023.2/_sources/samples/linear_regression.ipynb
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2023.2/_sources/samples/linear_regression.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/linear_regression.ipynb.txt b/2023.2/_sources/samples/linear_regression.ipynb.txt
index 4a356e6bfc..508ee06d8c 100644
--- a/2023.2/_sources/samples/linear_regression.ipynb.txt
+++ b/2023.2/_sources/samples/linear_regression.ipynb.txt
@@ -162,7 +162,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/logistictic_regression_cifar.ipynb b/2023.2/_sources/samples/logistictic_regression_cifar.ipynb
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2023.2/_sources/samples/logistictic_regression_cifar.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/logistictic_regression_cifar.ipynb.txt b/2023.2/_sources/samples/logistictic_regression_cifar.ipynb.txt
index f4cb2b95a7..43727804d7 100644
--- a/2023.2/_sources/samples/logistictic_regression_cifar.ipynb.txt
+++ b/2023.2/_sources/samples/logistictic_regression_cifar.ipynb.txt
@@ -108,7 +108,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/nusvr_medical_charges.ipynb b/2023.2/_sources/samples/nusvr_medical_charges.ipynb
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2023.2/_sources/samples/nusvr_medical_charges.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/nusvr_medical_charges.ipynb.txt b/2023.2/_sources/samples/nusvr_medical_charges.ipynb.txt
index bcfbe9154f..8c72c1b71d 100644
--- a/2023.2/_sources/samples/nusvr_medical_charges.ipynb.txt
+++ b/2023.2/_sources/samples/nusvr_medical_charges.ipynb.txt
@@ -133,7 +133,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/random_forest_yolanda.ipynb b/2023.2/_sources/samples/random_forest_yolanda.ipynb
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2023.2/_sources/samples/random_forest_yolanda.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/random_forest_yolanda.ipynb.txt b/2023.2/_sources/samples/random_forest_yolanda.ipynb.txt
index a184b22fd1..276284ed9b 100644
--- a/2023.2/_sources/samples/random_forest_yolanda.ipynb.txt
+++ b/2023.2/_sources/samples/random_forest_yolanda.ipynb.txt
@@ -104,7 +104,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/ridge_regression.ipynb b/2023.2/_sources/samples/ridge_regression.ipynb
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2023.2/_sources/samples/ridge_regression.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/ridge_regression.ipynb.txt b/2023.2/_sources/samples/ridge_regression.ipynb.txt
index c3d0856d00..1c159a13ae 100644
--- a/2023.2/_sources/samples/ridge_regression.ipynb.txt
+++ b/2023.2/_sources/samples/ridge_regression.ipynb.txt
@@ -169,7 +169,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/2023.2/_sources/samples/svc_adult.ipynb b/2023.2/_sources/samples/svc_adult.ipynb
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2023.2/_sources/samples/svc_adult.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/samples/tsne.ipynb b/2023.2/_sources/samples/tsne.ipynb
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2023.2/_sources/samples/tsne.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2023.2/_sources/support.rst b/2023.2/_sources/support.rst
new file mode 100644
index 0000000000..15d495c864
--- /dev/null
+++ b/2023.2/_sources/support.rst
@@ -0,0 +1,25 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Support
+--------------------
+Report issues, ask questions, and provide suggestions using:
+
+- `GitHub Issues <https://github.com/intel/scikit-learn-intelex/issues>`_
+- `GitHub Discussions <https://github.com/intel/scikit-learn-intelex/discussions>`_
+- `Forum <https://community.intel.com/t5/Intel-Distribution-for-Python/bd-p/distribution-python>`_
+
+You may reach out to project maintainers privately at onedal.maintainers@intel.com
diff --git a/latest/_sources/system-requirements.rst.txt b/2023.2/_sources/system-requirements.rst
similarity index 100%
rename from latest/_sources/system-requirements.rst.txt
rename to 2023.2/_sources/system-requirements.rst
diff --git a/2023.2/_sources/verbose.rst b/2023.2/_sources/verbose.rst
new file mode 100644
index 0000000000..1eee3c6e4b
--- /dev/null
+++ b/2023.2/_sources/verbose.rst
@@ -0,0 +1,54 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _verbose:
+
+############
+Verbose Mode
+############
+
+Use |intelex| in verbose mode to find out which implementation of the algorithm is currently used,
+|intelex| or original Scikit-learn.
+
+.. note:: Verbose mode is only available for :ref:`supported algorithms <sklearn_algorithms>`.
+
+To enable verbose mode, set the ``SKLEARNEX_VERBOSE`` environment variable as shown below:
+
+- On Linux and MacOS::
+
+     export SKLEARNEX_VERBOSE=INFO
+
+- On Windows::
+
+     set SKLEARNEX_VERBOSE=INFO
+
+Alternatively, get |intelex| logger and set its logging level in the Python code::
+
+     import logging
+     logger = logging.getLogger('sklearnex')
+     logger.setLevel(logging.INFO)
+
+During the calls that use Intel-optimized scikit-learn, you will receive additional print statements
+that indicate which implementation is being called.
+These print statements are only available for :ref:`supported algorithms <sklearn_algorithms>`.
+
+For example, for DBSCAN you get one of these print statements depending on which implementation is used::
+
+    INFO:sklearnex: sklearn.cluster.DBSCAN.fit: running accelerated version on CPU
+
+::
+
+    INFO:sklearnex: sklearn.cluster.DBSCAN.fit: fallback to original Scikit-learn
diff --git a/2023.2/_sources/what-is-patching.rst b/2023.2/_sources/what-is-patching.rst
new file mode 100644
index 0000000000..db864390b2
--- /dev/null
+++ b/2023.2/_sources/what-is-patching.rst
@@ -0,0 +1,57 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _what_is_patching:
+
+########
+Patching
+########
+
+.. glossary::
+
+   patching
+      To patch scikit-learn with |intelex| is to replace stock scikit-learn algorithms
+      with their optimized versions provided by the extension. You can always :term:`undo the patch <unpatching>`.
+
+      There are different ways to patch scikit-learn:
+
+      .. include:: /patching/patching-options.rst
+
+      .. seealso:: :ref:`get_started`
+
+   global patching
+      One of the patching options available in |intelex|.
+      With global patching, you can patch all scikit-learn applications at once::
+
+         python -m sklearnex.glob patch_sklearn
+      
+      .. seealso:: :ref:`global_patching`
+   
+   unpatching
+      To undo the patch is to return to the use of original scikit-learn implementation and
+      replace patched algorithms with the stock scikit-learn algorithms.
+      Unpatching requires scikit-learn to be re-imported again:
+
+      .. include:: /patching/undo-patch.rst
+
+How it works
+------------
+
+The extension replaces the original estimators in scikit-learn modules with the optimized ones.
+If the desired algorithm parameters are not supported by the |intelex|,
+then the result of the original scikit-learn is returned.
+
+.. seealso:: :ref:`verbose`
\ No newline at end of file
diff --git a/2023.2/_sources/what-is-patching.rst.txt b/2023.2/_sources/what-is-patching.rst.txt
index af993f3a3c..db864390b2 100644
--- a/2023.2/_sources/what-is-patching.rst.txt
+++ b/2023.2/_sources/what-is-patching.rst.txt
@@ -30,9 +30,15 @@ Patching
 
       .. include:: /patching/patching-options.rst
 
+      .. seealso:: :ref:`get_started`
+
    global patching
-   
-      .. include:: global-patching.rst
+      One of the patching options available in |intelex|.
+      With global patching, you can patch all scikit-learn applications at once::
+
+         python -m sklearnex.glob patch_sklearn
+      
+      .. seealso:: :ref:`global_patching`
    
    unpatching
       To undo the patch is to return to the use of original scikit-learn implementation and
diff --git a/2023.2/_static/_sphinx_javascript_frameworks_compat.js b/2023.2/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000000..81415803ec
--- /dev/null
+++ b/2023.2/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/2023.2/_static/css/badge_only.css b/2023.2/_static/css/badge_only.css
index c718cee441..88ba55b965 100644
--- a/2023.2/_static/css/badge_only.css
+++ b/2023.2/_static/css/badge_only.css
@@ -1 +1 @@
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\ No newline at end of file
+.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions .rst-other-versions .rtd-current-item{font-weight:700}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}#flyout-search-form{padding:6px}
\ No newline at end of file
diff --git a/2023.2/_static/css/theme.css b/2023.2/_static/css/theme.css
index c03c88f06c..0f14f10646 100644
--- a/2023.2/_static/css/theme.css
+++ b/2023.2/_static/css/theme.css
@@ -1,4 +1,4 @@
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index b34fb6d5aa..1db55bd294 100644
--- a/2023.2/_static/custom.css
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-section {
-    display:block;
-    float: left;
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+/* override table width restrictions */
+@media screen and (min-width: 767px) {
+
+   .wy-table-responsive table td {
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+
+
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+  width: 100%;
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+
+table.optimization-notice td {
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+
+table.optimization-notice td p:last-child {
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+
+img.with-border {
+   border:1px solid #021a40;
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+
+div.column {
+   float: left;
+   width: 50%;
+   padding: 10px;
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+/* Clear floats after the columns */
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+ content: "";
+ display: table;
+ clear: both;
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+
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+div.admonition-container {
+ width: 49%;
+ padding: 0 3px 0 0;
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+
+/* Clear floats after the columns */
+.admonition-container:after {
+ content: "";
+ display: table;
+ clear: both;
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+div.quotation {
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+  padding: 1em 0 0 1em;
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+   display: inline-block;
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rename from _css/fonts/lato-normal.woff2
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rename from _css/fonts/Roboto-Slab-Regular.woff2
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new file mode 100644
index 0000000000..45fecf7511
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+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 44.4 44.4" style="enable-background:new 0 0 44.4 44.4;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:none;stroke:#F5A252;stroke-width:5;stroke-miterlimit:10;}
+	.st1{fill:none;stroke:#579ACA;stroke-width:5;stroke-miterlimit:10;}
+	.st2{fill:none;stroke:#E66581;stroke-width:5;stroke-miterlimit:10;}
+</style>
+<title>logo</title>
+<g>
+	<path class="st0" d="M33.9,6.4c3.6,3.9,3.4,9.9-0.5,13.5s-9.9,3.4-13.5-0.5s-3.4-9.9,0.5-13.5l0,0C24.2,2.4,30.2,2.6,33.9,6.4z"/>
+	<path class="st1" d="M35.1,27.3c2.6,4.6,1.1,10.4-3.5,13c-4.6,2.6-10.4,1.1-13-3.5s-1.1-10.4,3.5-13l0,0
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+	<path class="st2" d="M25.9,17.8c2.6,4.6,1.1,10.4-3.5,13s-10.4,1.1-13-3.5s-1.1-10.4,3.5-13l0,0C17.5,11.7,23.3,13.2,25.9,17.8z"/>
+	<path class="st1" d="M19.2,26.4c3.1-4.3,9.1-5.2,13.3-2.1c1.1,0.8,2,1.8,2.7,3"/>
+	<path class="st0" d="M19.9,19.4c-3.6-3.9-3.4-9.9,0.5-13.5s9.9-3.4,13.5,0.5"/>
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new file mode 100644
index 0000000000..60cfe9f222
--- /dev/null
+++ b/2023.2/_static/images/logo_jupyterhub.svg
@@ -0,0 +1 @@
+<svg id="Layer_1" data-name="Layer 1" xmlns="http://www.w3.org/2000/svg" width="38.73" height="50" viewBox="0 0 38.73 50"><defs><style>.cls-1{fill:#767677;}.cls-2{fill:#f37726;}.cls-3{fill:#9e9e9e;}.cls-4{fill:#616262;}.cls-5{font-size:17.07px;fill:#fff;font-family:Roboto-Regular, Roboto;}</style></defs><title>logo_jupyterhub</title><g id="Canvas"><path id="path7_fill" data-name="path7 fill" class="cls-1" d="M39.51,3.53a3,3,0,0,1-1.7,2.9A3,3,0,0,1,34.48,6a3,3,0,0,1-.82-3.26,3,3,0,0,1,1.05-1.41A3,3,0,0,1,37.52.86a2.88,2.88,0,0,1,1,.6,3,3,0,0,1,.7.93,3.18,3.18,0,0,1,.28,1.14Z" transform="translate(-1.87 -0.69)"/><path id="path8_fill" data-name="path8 fill" class="cls-2" d="M21.91,38.39c-8,0-15.06-2.87-18.7-7.12a19.93,19.93,0,0,0,37.39,0C37,35.52,30,38.39,21.91,38.39Z" transform="translate(-1.87 -0.69)"/><path id="path9_fill" data-name="path9 fill" class="cls-2" d="M21.91,10.78c8,0,15.05,2.87,18.69,7.12a19.93,19.93,0,0,0-37.39,0C6.85,13.64,13.86,10.78,21.91,10.78Z" transform="translate(-1.87 -0.69)"/><path id="path10_fill" data-name="path10 fill" class="cls-3" d="M10.88,46.66a3.86,3.86,0,0,1-.52,2.15,3.81,3.81,0,0,1-1.62,1.51,3.93,3.93,0,0,1-2.19.34,3.79,3.79,0,0,1-2-.94,3.73,3.73,0,0,1-1.14-1.9,3.79,3.79,0,0,1,.1-2.21,3.86,3.86,0,0,1,1.33-1.78,3.92,3.92,0,0,1,3.54-.53,3.85,3.85,0,0,1,2.14,1.93,3.74,3.74,0,0,1,.37,1.43Z" transform="translate(-1.87 -0.69)"/><path id="path11_fill" data-name="path11 fill" class="cls-4" d="M4.12,9.81A2.18,2.18,0,0,1,2.9,9.48a2.23,2.23,0,0,1-.84-1A2.26,2.26,0,0,1,1.9,7.26a2.13,2.13,0,0,1,.56-1.13,2.18,2.18,0,0,1,2.36-.56,2.13,2.13,0,0,1,1,.76,2.18,2.18,0,0,1,.42,1.2A2.22,2.22,0,0,1,4.12,9.81Z" transform="translate(-1.87 -0.69)"/></g><text class="cls-5" transform="translate(5.24 30.01)">Hub</text></svg>
diff --git a/_scripts/jquery.js b/2023.2/_static/jquery.js
similarity index 100%
rename from _scripts/jquery.js
rename to 2023.2/_static/jquery.js
diff --git a/2023.2/_static/js/html5shiv-printshiv.min.js b/2023.2/_static/js/html5shiv-printshiv.min.js
deleted file mode 100644
index 2b43bd062e..0000000000
--- a/2023.2/_static/js/html5shiv-printshiv.min.js
+++ /dev/null
@@ -1,4 +0,0 @@
-/**
-* @preserve HTML5 Shiv 3.7.3-pre | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
-*/
-!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=y.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=y.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),y.elements=c+" "+a,j(b)}function f(a){var b=x[a[v]];return b||(b={},w++,a[v]=w,x[w]=b),b}function g(a,c,d){if(c||(c=b),q)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():u.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||t.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),q)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return y.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(y,b.frag)}function j(a){a||(a=b);var d=f(a);return!y.shivCSS||p||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),q||i(a,d),a}function k(a){for(var b,c=a.getElementsByTagName("*"),e=c.length,f=RegExp("^(?:"+d().join("|")+")$","i"),g=[];e--;)b=c[e],f.test(b.nodeName)&&g.push(b.applyElement(l(b)));return g}function l(a){for(var b,c=a.attributes,d=c.length,e=a.ownerDocument.createElement(A+":"+a.nodeName);d--;)b=c[d],b.specified&&e.setAttribute(b.nodeName,b.nodeValue);return e.style.cssText=a.style.cssText,e}function m(a){for(var b,c=a.split("{"),e=c.length,f=RegExp("(^|[\\s,>+~])("+d().join("|")+")(?=[[\\s,>+~#.:]|$)","gi"),g="$1"+A+"\\:$2";e--;)b=c[e]=c[e].split("}"),b[b.length-1]=b[b.length-1].replace(f,g),c[e]=b.join("}");return c.join("{")}function n(a){for(var b=a.length;b--;)a[b].removeNode()}function o(a){function b(){clearTimeout(g._removeSheetTimer),d&&d.removeNode(!0),d=null}var d,e,g=f(a),h=a.namespaces,i=a.parentWindow;return!B||a.printShived?a:("undefined"==typeof h[A]&&h.add(A),i.attachEvent("onbeforeprint",function(){b();for(var f,g,h,i=a.styleSheets,j=[],l=i.length,n=Array(l);l--;)n[l]=i[l];for(;h=n.pop();)if(!h.disabled&&z.test(h.media)){try{f=h.imports,g=f.length}catch(o){g=0}for(l=0;g>l;l++)n.push(f[l]);try{j.push(h.cssText)}catch(o){}}j=m(j.reverse().join("")),e=k(a),d=c(a,j)}),i.attachEvent("onafterprint",function(){n(e),clearTimeout(g._removeSheetTimer),g._removeSheetTimer=setTimeout(b,500)}),a.printShived=!0,a)}var p,q,r="3.7.3",s=a.html5||{},t=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,u=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,v="_html5shiv",w=0,x={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",p="hidden"in a,q=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){p=!0,q=!0}}();var y={elements:s.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:r,shivCSS:s.shivCSS!==!1,supportsUnknownElements:q,shivMethods:s.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=y,j(b);var z=/^$|\b(?:all|print)\b/,A="html5shiv",B=!q&&function(){var c=b.documentElement;return!("undefined"==typeof b.namespaces||"undefined"==typeof b.parentWindow||"undefined"==typeof c.applyElement||"undefined"==typeof c.removeNode||"undefined"==typeof a.attachEvent)}();y.type+=" print",y.shivPrint=o,o(b),"object"==typeof module&&module.exports&&(module.exports=y)}("undefined"!=typeof window?window:this,document);
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diff --git a/2023.2/_static/js/html5shiv.min.js b/2023.2/_static/js/html5shiv.min.js
deleted file mode 100644
index cd1c674f5e..0000000000
--- a/2023.2/_static/js/html5shiv.min.js
+++ /dev/null
@@ -1,4 +0,0 @@
-/**
-* @preserve HTML5 Shiv 3.7.3 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
-*/
-!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3-pre",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b),"object"==typeof module&&module.exports&&(module.exports=t)}("undefined"!=typeof window?window:this,document);
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diff --git a/2023.2/_static/js/versions.js b/2023.2/_static/js/versions.js
new file mode 100644
index 0000000000..818bc9969f
--- /dev/null
+++ b/2023.2/_static/js/versions.js
@@ -0,0 +1,224 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = "True";
+const themeLanguageSelector = "True";
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${config.projects.translations
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/2023.2/_static/locales/ar/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ar/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..15541a6a37
Binary files /dev/null and b/2023.2/_static/locales/ar/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ar/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ar/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..2e8d682024
--- /dev/null
+++ b/2023.2/_static/locales/ar/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ar\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "أقترح تحرير"
+
+msgid "Last updated on"
+msgstr "آخر تحديث في"
+
+msgid "Edit this page"
+msgstr "قم بتحرير هذه الصفحة"
+
+msgid "Launch"
+msgstr "إطلاق"
+
+msgid "Print to PDF"
+msgstr "طباعة إلى PDF"
+
+msgid "open issue"
+msgstr "قضية مفتوحة"
+
+msgid "Download notebook file"
+msgstr "تنزيل ملف دفتر الملاحظات"
+
+msgid "Toggle navigation"
+msgstr "تبديل التنقل"
+
+msgid "Source repository"
+msgstr "مستودع المصدر"
+
+msgid "By the"
+msgstr "بواسطة"
+
+msgid "next page"
+msgstr "الصفحة التالية"
+
+msgid "repository"
+msgstr "مخزن"
+
+msgid "Sphinx Book Theme"
+msgstr "موضوع كتاب أبو الهول"
+
+msgid "Download source file"
+msgstr "تنزيل ملف المصدر"
+
+msgid "Contents"
+msgstr "محتويات"
+
+msgid "By"
+msgstr "بواسطة"
+
+msgid "Copyright"
+msgstr "حقوق النشر"
+
+msgid "Fullscreen mode"
+msgstr "وضع ملء الشاشة"
+
+msgid "Open an issue"
+msgstr "افتح قضية"
+
+msgid "previous page"
+msgstr "الصفحة السابقة"
+
+msgid "Download this page"
+msgstr "قم بتنزيل هذه الصفحة"
+
+msgid "Theme by the"
+msgstr "موضوع بواسطة"
diff --git a/2023.2/_static/locales/bg/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/bg/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..da95120037
Binary files /dev/null and b/2023.2/_static/locales/bg/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/bg/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/bg/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..56ef0ebdfa
--- /dev/null
+++ b/2023.2/_static/locales/bg/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: bg\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "предложи редактиране"
+
+msgid "Last updated on"
+msgstr "Последна актуализация на"
+
+msgid "Edit this page"
+msgstr "Редактирайте тази страница"
+
+msgid "Launch"
+msgstr "Стартиране"
+
+msgid "Print to PDF"
+msgstr "Печат в PDF"
+
+msgid "open issue"
+msgstr "отворен брой"
+
+msgid "Download notebook file"
+msgstr "Изтеглете файла на бележника"
+
+msgid "Toggle navigation"
+msgstr "Превключване на навигацията"
+
+msgid "Source repository"
+msgstr "Хранилище на източника"
+
+msgid "By the"
+msgstr "По"
+
+msgid "next page"
+msgstr "Следваща страница"
+
+msgid "repository"
+msgstr "хранилище"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема на книгата Sphinx"
+
+msgid "Download source file"
+msgstr "Изтеглете изходния файл"
+
+msgid "Contents"
+msgstr "Съдържание"
+
+msgid "By"
+msgstr "От"
+
+msgid "Copyright"
+msgstr "Авторско право"
+
+msgid "Fullscreen mode"
+msgstr "Режим на цял екран"
+
+msgid "Open an issue"
+msgstr "Отворете проблем"
+
+msgid "previous page"
+msgstr "предишна страница"
+
+msgid "Download this page"
+msgstr "Изтеглете тази страница"
+
+msgid "Theme by the"
+msgstr "Тема от"
diff --git a/2023.2/_static/locales/bn/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/bn/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..6b96639b72
Binary files /dev/null and b/2023.2/_static/locales/bn/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/bn/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/bn/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..243ca31f73
--- /dev/null
+++ b/2023.2/_static/locales/bn/LC_MESSAGES/booktheme.po
@@ -0,0 +1,63 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: bn\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Last updated on"
+msgstr "সর্বশেষ আপডেট"
+
+msgid "Edit this page"
+msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন"
+
+msgid "Launch"
+msgstr "শুরু করা"
+
+msgid "Print to PDF"
+msgstr "পিডিএফ প্রিন্ট করুন"
+
+msgid "open issue"
+msgstr "খোলা সমস্যা"
+
+msgid "Download notebook file"
+msgstr "নোটবুক ফাইল ডাউনলোড করুন"
+
+msgid "Toggle navigation"
+msgstr "নেভিগেশন টগল করুন"
+
+msgid "Source repository"
+msgstr "উত্স সংগ্রহস্থল"
+
+msgid "By the"
+msgstr "দ্বারা"
+
+msgid "next page"
+msgstr "পরবর্তী পৃষ্ঠা"
+
+msgid "Sphinx Book Theme"
+msgstr "স্পিনিক্স বুক থিম"
+
+msgid "Download source file"
+msgstr "উত্স ফাইল ডাউনলোড করুন"
+
+msgid "By"
+msgstr "দ্বারা"
+
+msgid "Copyright"
+msgstr "কপিরাইট"
+
+msgid "Open an issue"
+msgstr "একটি সমস্যা খুলুন"
+
+msgid "previous page"
+msgstr "আগের পৃষ্ঠা"
+
+msgid "Download this page"
+msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন"
+
+msgid "Theme by the"
+msgstr "থিম দ্বারা"
diff --git a/2023.2/_static/locales/ca/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ca/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..a4dd30e9bd
Binary files /dev/null and b/2023.2/_static/locales/ca/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ca/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ca/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..b27a13db9c
--- /dev/null
+++ b/2023.2/_static/locales/ca/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ca\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "suggerir edició"
+
+msgid "Last updated on"
+msgstr "Darrera actualització el"
+
+msgid "Edit this page"
+msgstr "Editeu aquesta pàgina"
+
+msgid "Launch"
+msgstr "Llançament"
+
+msgid "Print to PDF"
+msgstr "Imprimeix a PDF"
+
+msgid "open issue"
+msgstr "número obert"
+
+msgid "Download notebook file"
+msgstr "Descarregar fitxer de quadern"
+
+msgid "Toggle navigation"
+msgstr "Commuta la navegació"
+
+msgid "Source repository"
+msgstr "Dipòsit de fonts"
+
+msgid "By the"
+msgstr "Per la"
+
+msgid "next page"
+msgstr "pàgina següent"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema del llibre Esfinx"
+
+msgid "Download source file"
+msgstr "Baixeu el fitxer font"
+
+msgid "By"
+msgstr "Per"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Open an issue"
+msgstr "Obriu un número"
+
+msgid "previous page"
+msgstr "Pàgina anterior"
+
+msgid "Download this page"
+msgstr "Descarregueu aquesta pàgina"
+
+msgid "Theme by the"
+msgstr "Tema del"
diff --git a/2023.2/_static/locales/cs/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/cs/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..c39e01a6ae
Binary files /dev/null and b/2023.2/_static/locales/cs/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/cs/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/cs/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..3818df9765
--- /dev/null
+++ b/2023.2/_static/locales/cs/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: cs\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "navrhnout úpravy"
+
+msgid "Last updated on"
+msgstr "Naposledy aktualizováno"
+
+msgid "Edit this page"
+msgstr "Upravit tuto stránku"
+
+msgid "Launch"
+msgstr "Zahájení"
+
+msgid "Print to PDF"
+msgstr "Tisk do PDF"
+
+msgid "open issue"
+msgstr "otevřené číslo"
+
+msgid "Download notebook file"
+msgstr "Stáhnout soubor poznámkového bloku"
+
+msgid "Toggle navigation"
+msgstr "Přepnout navigaci"
+
+msgid "Source repository"
+msgstr "Zdrojové úložiště"
+
+msgid "By the"
+msgstr "Podle"
+
+msgid "next page"
+msgstr "další strana"
+
+msgid "repository"
+msgstr "úložiště"
+
+msgid "Sphinx Book Theme"
+msgstr "Téma knihy Sfinga"
+
+msgid "Download source file"
+msgstr "Stáhněte si zdrojový soubor"
+
+msgid "Contents"
+msgstr "Obsah"
+
+msgid "By"
+msgstr "Podle"
+
+msgid "Copyright"
+msgstr "autorská práva"
+
+msgid "Fullscreen mode"
+msgstr "Režim celé obrazovky"
+
+msgid "Open an issue"
+msgstr "Otevřete problém"
+
+msgid "previous page"
+msgstr "předchozí stránka"
+
+msgid "Download this page"
+msgstr "Stáhněte si tuto stránku"
+
+msgid "Theme by the"
+msgstr "Téma od"
diff --git a/2023.2/_static/locales/da/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/da/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..f43157d70c
Binary files /dev/null and b/2023.2/_static/locales/da/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/da/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/da/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..7f20a3bd09
--- /dev/null
+++ b/2023.2/_static/locales/da/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: da\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "foreslå redigering"
+
+msgid "Last updated on"
+msgstr "Sidst opdateret den"
+
+msgid "Edit this page"
+msgstr "Rediger denne side"
+
+msgid "Launch"
+msgstr "Start"
+
+msgid "Print to PDF"
+msgstr "Udskriv til PDF"
+
+msgid "open issue"
+msgstr "åbent nummer"
+
+msgid "Download notebook file"
+msgstr "Download notesbog-fil"
+
+msgid "Toggle navigation"
+msgstr "Skift navigation"
+
+msgid "Source repository"
+msgstr "Kildelager"
+
+msgid "By the"
+msgstr "Ved"
+
+msgid "next page"
+msgstr "Næste side"
+
+msgid "repository"
+msgstr "lager"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx bogtema"
+
+msgid "Download source file"
+msgstr "Download kildefil"
+
+msgid "Contents"
+msgstr "Indhold"
+
+msgid "By"
+msgstr "Ved"
+
+msgid "Copyright"
+msgstr "ophavsret"
+
+msgid "Fullscreen mode"
+msgstr "Fuldskærmstilstand"
+
+msgid "Open an issue"
+msgstr "Åbn et problem"
+
+msgid "previous page"
+msgstr "forrige side"
+
+msgid "Download this page"
+msgstr "Download denne side"
+
+msgid "Theme by the"
+msgstr "Tema af"
diff --git a/2023.2/_static/locales/de/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/de/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..648b565c78
Binary files /dev/null and b/2023.2/_static/locales/de/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/de/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/de/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..c0027d3ab0
--- /dev/null
+++ b/2023.2/_static/locales/de/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: de\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "vorschlagen zu bearbeiten"
+
+msgid "Last updated on"
+msgstr "Zuletzt aktualisiert am"
+
+msgid "Edit this page"
+msgstr "Bearbeite diese Seite"
+
+msgid "Launch"
+msgstr "Starten"
+
+msgid "Print to PDF"
+msgstr "In PDF drucken"
+
+msgid "open issue"
+msgstr "offenes Thema"
+
+msgid "Download notebook file"
+msgstr "Notebook-Datei herunterladen"
+
+msgid "Toggle navigation"
+msgstr "Navigation umschalten"
+
+msgid "Source repository"
+msgstr "Quell-Repository"
+
+msgid "By the"
+msgstr "Bis zum"
+
+msgid "next page"
+msgstr "Nächste Seite"
+
+msgid "repository"
+msgstr "Repository"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx-Buch-Thema"
+
+msgid "Download source file"
+msgstr "Quelldatei herunterladen"
+
+msgid "Contents"
+msgstr "Inhalt"
+
+msgid "By"
+msgstr "Durch"
+
+msgid "Copyright"
+msgstr "Urheberrechte ©"
+
+msgid "Fullscreen mode"
+msgstr "Vollbildmodus"
+
+msgid "Open an issue"
+msgstr "Öffnen Sie ein Problem"
+
+msgid "previous page"
+msgstr "vorherige Seite"
+
+msgid "Download this page"
+msgstr "Laden Sie diese Seite herunter"
+
+msgid "Theme by the"
+msgstr "Thema von der"
diff --git a/2023.2/_static/locales/el/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/el/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..fca6e9355f
Binary files /dev/null and b/2023.2/_static/locales/el/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/el/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/el/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..bdeb3270aa
--- /dev/null
+++ b/2023.2/_static/locales/el/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: el\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "προτείνω επεξεργασία"
+
+msgid "Last updated on"
+msgstr "Τελευταία ενημέρωση στις"
+
+msgid "Edit this page"
+msgstr "Επεξεργαστείτε αυτήν τη σελίδα"
+
+msgid "Launch"
+msgstr "Εκτόξευση"
+
+msgid "Print to PDF"
+msgstr "Εκτύπωση σε PDF"
+
+msgid "open issue"
+msgstr "ανοιχτό ζήτημα"
+
+msgid "Download notebook file"
+msgstr "Λήψη αρχείου σημειωματάριου"
+
+msgid "Toggle navigation"
+msgstr "Εναλλαγή πλοήγησης"
+
+msgid "Source repository"
+msgstr "Αποθήκη πηγής"
+
+msgid "By the"
+msgstr "Από το"
+
+msgid "next page"
+msgstr "επόμενη σελίδα"
+
+msgid "repository"
+msgstr "αποθήκη"
+
+msgid "Sphinx Book Theme"
+msgstr "Θέμα βιβλίου Sphinx"
+
+msgid "Download source file"
+msgstr "Λήψη αρχείου προέλευσης"
+
+msgid "Contents"
+msgstr "Περιεχόμενα"
+
+msgid "By"
+msgstr "Με"
+
+msgid "Copyright"
+msgstr "Πνευματική ιδιοκτησία"
+
+msgid "Fullscreen mode"
+msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ"
+
+msgid "Open an issue"
+msgstr "Ανοίξτε ένα ζήτημα"
+
+msgid "previous page"
+msgstr "προηγούμενη σελίδα"
+
+msgid "Download this page"
+msgstr "Λήψη αυτής της σελίδας"
+
+msgid "Theme by the"
+msgstr "Θέμα από το"
diff --git a/2023.2/_static/locales/eo/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/eo/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..d1072bbec6
Binary files /dev/null and b/2023.2/_static/locales/eo/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/eo/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/eo/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..6749f3a34a
--- /dev/null
+++ b/2023.2/_static/locales/eo/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: eo\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "sugesti redaktadon"
+
+msgid "Last updated on"
+msgstr "Laste ĝisdatigita la"
+
+msgid "Edit this page"
+msgstr "Redaktu ĉi tiun paĝon"
+
+msgid "Launch"
+msgstr "Lanĉo"
+
+msgid "Print to PDF"
+msgstr "Presi al PDF"
+
+msgid "open issue"
+msgstr "malferma numero"
+
+msgid "Download notebook file"
+msgstr "Elŝutu kajeran dosieron"
+
+msgid "Toggle navigation"
+msgstr "Ŝalti navigadon"
+
+msgid "Source repository"
+msgstr "Fonto-deponejo"
+
+msgid "By the"
+msgstr "Per la"
+
+msgid "next page"
+msgstr "sekva paĝo"
+
+msgid "repository"
+msgstr "deponejo"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinksa Libro-Temo"
+
+msgid "Download source file"
+msgstr "Elŝutu fontodosieron"
+
+msgid "Contents"
+msgstr "Enhavo"
+
+msgid "By"
+msgstr "De"
+
+msgid "Copyright"
+msgstr "Kopirajto"
+
+msgid "Fullscreen mode"
+msgstr "Plenekrana reĝimo"
+
+msgid "Open an issue"
+msgstr "Malfermu numeron"
+
+msgid "previous page"
+msgstr "antaŭa paĝo"
+
+msgid "Download this page"
+msgstr "Elŝutu ĉi tiun paĝon"
+
+msgid "Theme by the"
+msgstr "Temo de la"
diff --git a/2023.2/_static/locales/es/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/es/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..ba2ee4dc22
Binary files /dev/null and b/2023.2/_static/locales/es/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/es/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/es/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..71dde37f27
--- /dev/null
+++ b/2023.2/_static/locales/es/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: es\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "sugerir editar"
+
+msgid "Last updated on"
+msgstr "Ultima actualización en"
+
+msgid "Edit this page"
+msgstr "Edita esta página"
+
+msgid "Launch"
+msgstr "Lanzamiento"
+
+msgid "Print to PDF"
+msgstr "Imprimir en PDF"
+
+msgid "open issue"
+msgstr "Tema abierto"
+
+msgid "Download notebook file"
+msgstr "Descargar archivo de cuaderno"
+
+msgid "Toggle navigation"
+msgstr "Navegación de palanca"
+
+msgid "Source repository"
+msgstr "Repositorio de origen"
+
+msgid "By the"
+msgstr "Por el"
+
+msgid "next page"
+msgstr "siguiente página"
+
+msgid "repository"
+msgstr "repositorio"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema del libro de la esfinge"
+
+msgid "Download source file"
+msgstr "Descargar archivo fuente"
+
+msgid "Contents"
+msgstr "Contenido"
+
+msgid "By"
+msgstr "Por"
+
+msgid "Copyright"
+msgstr "Derechos de autor"
+
+msgid "Fullscreen mode"
+msgstr "Modo de pantalla completa"
+
+msgid "Open an issue"
+msgstr "Abrir un problema"
+
+msgid "previous page"
+msgstr "pagina anterior"
+
+msgid "Download this page"
+msgstr "Descarga esta pagina"
+
+msgid "Theme by the"
+msgstr "Tema por el"
diff --git a/2023.2/_static/locales/et/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/et/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..983b82391f
Binary files /dev/null and b/2023.2/_static/locales/et/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/et/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/et/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..cdcd07c7d8
--- /dev/null
+++ b/2023.2/_static/locales/et/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: et\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "soovita muuta"
+
+msgid "Last updated on"
+msgstr "Viimati uuendatud"
+
+msgid "Edit this page"
+msgstr "Muutke seda lehte"
+
+msgid "Launch"
+msgstr "Käivitage"
+
+msgid "Print to PDF"
+msgstr "Prindi PDF-i"
+
+msgid "open issue"
+msgstr "avatud küsimus"
+
+msgid "Download notebook file"
+msgstr "Laadige sülearvuti fail alla"
+
+msgid "Toggle navigation"
+msgstr "Lülita navigeerimine sisse"
+
+msgid "Source repository"
+msgstr "Allikahoidla"
+
+msgid "By the"
+msgstr "Autor"
+
+msgid "next page"
+msgstr "järgmine leht"
+
+msgid "repository"
+msgstr "hoidla"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinksiraamatu teema"
+
+msgid "Download source file"
+msgstr "Laadige alla lähtefail"
+
+msgid "Contents"
+msgstr "Sisu"
+
+msgid "By"
+msgstr "Kõrval"
+
+msgid "Copyright"
+msgstr "Autoriõigus"
+
+msgid "Fullscreen mode"
+msgstr "Täisekraanirežiim"
+
+msgid "Open an issue"
+msgstr "Avage probleem"
+
+msgid "previous page"
+msgstr "eelmine leht"
+
+msgid "Download this page"
+msgstr "Laadige see leht alla"
+
+msgid "Theme by the"
+msgstr "Teema"
diff --git a/2023.2/_static/locales/fi/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/fi/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..d8ac054597
Binary files /dev/null and b/2023.2/_static/locales/fi/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/fi/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/fi/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..3c3dd08962
--- /dev/null
+++ b/2023.2/_static/locales/fi/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: fi\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "ehdottaa muokkausta"
+
+msgid "Last updated on"
+msgstr "Viimeksi päivitetty"
+
+msgid "Edit this page"
+msgstr "Muokkaa tätä sivua"
+
+msgid "Launch"
+msgstr "Tuoda markkinoille"
+
+msgid "Print to PDF"
+msgstr "Tulosta PDF-tiedostoon"
+
+msgid "open issue"
+msgstr "avoin ongelma"
+
+msgid "Download notebook file"
+msgstr "Lataa muistikirjatiedosto"
+
+msgid "Toggle navigation"
+msgstr "Vaihda navigointia"
+
+msgid "Source repository"
+msgstr "Lähteen arkisto"
+
+msgid "By the"
+msgstr "Mukaan"
+
+msgid "next page"
+msgstr "seuraava sivu"
+
+msgid "repository"
+msgstr "arkisto"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx-kirjan teema"
+
+msgid "Download source file"
+msgstr "Lataa lähdetiedosto"
+
+msgid "Contents"
+msgstr "Sisällys"
+
+msgid "By"
+msgstr "Tekijä"
+
+msgid "Copyright"
+msgstr "Tekijänoikeus"
+
+msgid "Fullscreen mode"
+msgstr "Koko näytön tila"
+
+msgid "Open an issue"
+msgstr "Avaa ongelma"
+
+msgid "previous page"
+msgstr "Edellinen sivu"
+
+msgid "Download this page"
+msgstr "Lataa tämä sivu"
+
+msgid "Theme by the"
+msgstr "Teeman tekijä"
diff --git a/2023.2/_static/locales/fr/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/fr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..f663d39f0f
Binary files /dev/null and b/2023.2/_static/locales/fr/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/fr/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/fr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..b57d2fe745
--- /dev/null
+++ b/2023.2/_static/locales/fr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: fr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "suggestion de modification"
+
+msgid "Last updated on"
+msgstr "Dernière mise à jour le"
+
+msgid "Edit this page"
+msgstr "Modifier cette page"
+
+msgid "Launch"
+msgstr "lancement"
+
+msgid "Print to PDF"
+msgstr "Imprimer au format PDF"
+
+msgid "open issue"
+msgstr "signaler un problème"
+
+msgid "Download notebook file"
+msgstr "Télécharger le fichier notebook"
+
+msgid "Toggle navigation"
+msgstr "Basculer la navigation"
+
+msgid "Source repository"
+msgstr "Dépôt source"
+
+msgid "By the"
+msgstr "Par le"
+
+msgid "next page"
+msgstr "page suivante"
+
+msgid "repository"
+msgstr "dépôt"
+
+msgid "Sphinx Book Theme"
+msgstr "Thème du livre Sphinx"
+
+msgid "Download source file"
+msgstr "Télécharger le fichier source"
+
+msgid "Contents"
+msgstr "Contenu"
+
+msgid "By"
+msgstr "Par"
+
+msgid "Copyright"
+msgstr "droits d'auteur"
+
+msgid "Fullscreen mode"
+msgstr "Mode plein écran"
+
+msgid "Open an issue"
+msgstr "Ouvrez un problème"
+
+msgid "previous page"
+msgstr "page précédente"
+
+msgid "Download this page"
+msgstr "Téléchargez cette page"
+
+msgid "Theme by the"
+msgstr "Thème par le"
diff --git a/2023.2/_static/locales/hr/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/hr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..eca4a1a284
Binary files /dev/null and b/2023.2/_static/locales/hr/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/hr/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/hr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..4c425e89ae
--- /dev/null
+++ b/2023.2/_static/locales/hr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: hr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "predloži uređivanje"
+
+msgid "Last updated on"
+msgstr "Posljednje ažuriranje:"
+
+msgid "Edit this page"
+msgstr "Uredite ovu stranicu"
+
+msgid "Launch"
+msgstr "Pokrenite"
+
+msgid "Print to PDF"
+msgstr "Ispis u PDF"
+
+msgid "open issue"
+msgstr "otvoreno izdanje"
+
+msgid "Download notebook file"
+msgstr "Preuzmi datoteku bilježnice"
+
+msgid "Toggle navigation"
+msgstr "Uključi / isključi navigaciju"
+
+msgid "Source repository"
+msgstr "Izvorno spremište"
+
+msgid "By the"
+msgstr "Od strane"
+
+msgid "next page"
+msgstr "sljedeća stranica"
+
+msgid "repository"
+msgstr "spremište"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema knjige Sphinx"
+
+msgid "Download source file"
+msgstr "Preuzmi izvornu datoteku"
+
+msgid "Contents"
+msgstr "Sadržaj"
+
+msgid "By"
+msgstr "Po"
+
+msgid "Copyright"
+msgstr "Autorska prava"
+
+msgid "Fullscreen mode"
+msgstr "Način preko cijelog zaslona"
+
+msgid "Open an issue"
+msgstr "Otvorite izdanje"
+
+msgid "previous page"
+msgstr "Prethodna stranica"
+
+msgid "Download this page"
+msgstr "Preuzmite ovu stranicu"
+
+msgid "Theme by the"
+msgstr "Tema autora"
diff --git a/2023.2/_static/locales/id/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/id/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..d07a06a9d2
Binary files /dev/null and b/2023.2/_static/locales/id/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/id/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/id/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..5db2ae1474
--- /dev/null
+++ b/2023.2/_static/locales/id/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: id\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "menyarankan edit"
+
+msgid "Last updated on"
+msgstr "Terakhir diperbarui saat"
+
+msgid "Edit this page"
+msgstr "Edit halaman ini"
+
+msgid "Launch"
+msgstr "Meluncurkan"
+
+msgid "Print to PDF"
+msgstr "Cetak ke PDF"
+
+msgid "open issue"
+msgstr "masalah terbuka"
+
+msgid "Download notebook file"
+msgstr "Unduh file notebook"
+
+msgid "Toggle navigation"
+msgstr "Alihkan navigasi"
+
+msgid "Source repository"
+msgstr "Repositori sumber"
+
+msgid "By the"
+msgstr "Oleh"
+
+msgid "next page"
+msgstr "halaman selanjutnya"
+
+msgid "repository"
+msgstr "gudang"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema Buku Sphinx"
+
+msgid "Download source file"
+msgstr "Unduh file sumber"
+
+msgid "Contents"
+msgstr "Isi"
+
+msgid "By"
+msgstr "Oleh"
+
+msgid "Copyright"
+msgstr "hak cipta"
+
+msgid "Fullscreen mode"
+msgstr "Mode layar penuh"
+
+msgid "Open an issue"
+msgstr "Buka masalah"
+
+msgid "previous page"
+msgstr "halaman sebelumnya"
+
+msgid "Download this page"
+msgstr "Unduh halaman ini"
+
+msgid "Theme by the"
+msgstr "Tema oleh"
diff --git a/2023.2/_static/locales/it/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/it/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..53ba476edd
Binary files /dev/null and b/2023.2/_static/locales/it/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/it/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/it/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..7d54fdefa8
--- /dev/null
+++ b/2023.2/_static/locales/it/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: it\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "suggerisci modifica"
+
+msgid "Last updated on"
+msgstr "Ultimo aggiornamento il"
+
+msgid "Edit this page"
+msgstr "Modifica questa pagina"
+
+msgid "Launch"
+msgstr "Lanciare"
+
+msgid "Print to PDF"
+msgstr "Stampa in PDF"
+
+msgid "open issue"
+msgstr "questione aperta"
+
+msgid "Download notebook file"
+msgstr "Scarica il file del taccuino"
+
+msgid "Toggle navigation"
+msgstr "Attiva / disattiva la navigazione"
+
+msgid "Source repository"
+msgstr "Repository di origine"
+
+msgid "By the"
+msgstr "Dal"
+
+msgid "next page"
+msgstr "pagina successiva"
+
+msgid "repository"
+msgstr "repository"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema del libro della Sfinge"
+
+msgid "Download source file"
+msgstr "Scarica il file sorgente"
+
+msgid "Contents"
+msgstr "Contenuti"
+
+msgid "By"
+msgstr "Di"
+
+msgid "Copyright"
+msgstr "Diritto d'autore"
+
+msgid "Fullscreen mode"
+msgstr "Modalità schermo intero"
+
+msgid "Open an issue"
+msgstr "Apri un problema"
+
+msgid "previous page"
+msgstr "pagina precedente"
+
+msgid "Download this page"
+msgstr "Scarica questa pagina"
+
+msgid "Theme by the"
+msgstr "Tema di"
diff --git a/2023.2/_static/locales/iw/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/iw/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..a45c6575e4
Binary files /dev/null and b/2023.2/_static/locales/iw/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/iw/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/iw/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..32b017cf69
--- /dev/null
+++ b/2023.2/_static/locales/iw/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: iw\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "מציע לערוך"
+
+msgid "Last updated on"
+msgstr "עודכן לאחרונה ב"
+
+msgid "Edit this page"
+msgstr "ערוך דף זה"
+
+msgid "Launch"
+msgstr "לְהַשִׁיק"
+
+msgid "Print to PDF"
+msgstr "הדפס לקובץ PDF"
+
+msgid "open issue"
+msgstr "בעיה פתוחה"
+
+msgid "Download notebook file"
+msgstr "הורד קובץ מחברת"
+
+msgid "Toggle navigation"
+msgstr "החלף ניווט"
+
+msgid "Source repository"
+msgstr "מאגר המקורות"
+
+msgid "By the"
+msgstr "דרך"
+
+msgid "next page"
+msgstr "עמוד הבא"
+
+msgid "repository"
+msgstr "מאגר"
+
+msgid "Sphinx Book Theme"
+msgstr "נושא ספר ספינקס"
+
+msgid "Download source file"
+msgstr "הורד את קובץ המקור"
+
+msgid "Contents"
+msgstr "תוכן"
+
+msgid "By"
+msgstr "על ידי"
+
+msgid "Copyright"
+msgstr "זכויות יוצרים"
+
+msgid "Fullscreen mode"
+msgstr "מצב מסך מלא"
+
+msgid "Open an issue"
+msgstr "פתח גיליון"
+
+msgid "previous page"
+msgstr "עמוד קודם"
+
+msgid "Download this page"
+msgstr "הורד דף זה"
+
+msgid "Theme by the"
+msgstr "נושא מאת"
diff --git a/2023.2/_static/locales/ja/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ja/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..1cefd29ce3
Binary files /dev/null and b/2023.2/_static/locales/ja/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ja/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ja/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..16924e1972
--- /dev/null
+++ b/2023.2/_static/locales/ja/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ja\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "編集を提案する"
+
+msgid "Last updated on"
+msgstr "最終更新日"
+
+msgid "Edit this page"
+msgstr "このページを編集"
+
+msgid "Launch"
+msgstr "起動"
+
+msgid "Print to PDF"
+msgstr "PDFに印刷"
+
+msgid "open issue"
+msgstr "未解決の問題"
+
+msgid "Download notebook file"
+msgstr "ノートブックファイルをダウンロード"
+
+msgid "Toggle navigation"
+msgstr "ナビゲーションを切り替え"
+
+msgid "Source repository"
+msgstr "ソースリポジトリ"
+
+msgid "By the"
+msgstr "によって"
+
+msgid "next page"
+msgstr "次のページ"
+
+msgid "repository"
+msgstr "リポジトリ"
+
+msgid "Sphinx Book Theme"
+msgstr "スフィンクスの本のテーマ"
+
+msgid "Download source file"
+msgstr "ソースファイルをダウンロード"
+
+msgid "Contents"
+msgstr "目次"
+
+msgid "By"
+msgstr "著者"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Fullscreen mode"
+msgstr "全画面モード"
+
+msgid "Open an issue"
+msgstr "問題を報告"
+
+msgid "previous page"
+msgstr "前のページ"
+
+msgid "Download this page"
+msgstr "このページをダウンロード"
+
+msgid "Theme by the"
+msgstr "のテーマ"
diff --git a/2023.2/_static/locales/ko/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ko/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..06c7ec938b
Binary files /dev/null and b/2023.2/_static/locales/ko/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ko/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ko/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..69dd18f773
--- /dev/null
+++ b/2023.2/_static/locales/ko/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ko\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "편집 제안"
+
+msgid "Last updated on"
+msgstr "마지막 업데이트"
+
+msgid "Edit this page"
+msgstr "이 페이지 편집"
+
+msgid "Launch"
+msgstr "시작하다"
+
+msgid "Print to PDF"
+msgstr "PDF로 인쇄"
+
+msgid "open issue"
+msgstr "열린 문제"
+
+msgid "Download notebook file"
+msgstr "노트북 파일 다운로드"
+
+msgid "Toggle navigation"
+msgstr "탐색 전환"
+
+msgid "Source repository"
+msgstr "소스 저장소"
+
+msgid "By the"
+msgstr "에 의해"
+
+msgid "next page"
+msgstr "다음 페이지"
+
+msgid "repository"
+msgstr "저장소"
+
+msgid "Sphinx Book Theme"
+msgstr "스핑크스 도서 테마"
+
+msgid "Download source file"
+msgstr "소스 파일 다운로드"
+
+msgid "Contents"
+msgstr "내용"
+
+msgid "By"
+msgstr "으로"
+
+msgid "Copyright"
+msgstr "저작권"
+
+msgid "Fullscreen mode"
+msgstr "전체 화면으로보기"
+
+msgid "Open an issue"
+msgstr "이슈 열기"
+
+msgid "previous page"
+msgstr "이전 페이지"
+
+msgid "Download this page"
+msgstr "이 페이지 다운로드"
+
+msgid "Theme by the"
+msgstr "테마별"
diff --git a/2023.2/_static/locales/lt/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/lt/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..4468ba04bc
Binary files /dev/null and b/2023.2/_static/locales/lt/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/lt/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/lt/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..9f037752c0
--- /dev/null
+++ b/2023.2/_static/locales/lt/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: lt\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "pasiūlyti redaguoti"
+
+msgid "Last updated on"
+msgstr "Paskutinį kartą atnaujinta"
+
+msgid "Edit this page"
+msgstr "Redaguoti šį puslapį"
+
+msgid "Launch"
+msgstr "Paleiskite"
+
+msgid "Print to PDF"
+msgstr "Spausdinti į PDF"
+
+msgid "open issue"
+msgstr "atviras klausimas"
+
+msgid "Download notebook file"
+msgstr "Atsisiųsti nešiojamojo kompiuterio failą"
+
+msgid "Toggle navigation"
+msgstr "Perjungti naršymą"
+
+msgid "Source repository"
+msgstr "Šaltinio saugykla"
+
+msgid "By the"
+msgstr "Prie"
+
+msgid "next page"
+msgstr "Kitas puslapis"
+
+msgid "repository"
+msgstr "saugykla"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinkso knygos tema"
+
+msgid "Download source file"
+msgstr "Atsisiųsti šaltinio failą"
+
+msgid "Contents"
+msgstr "Turinys"
+
+msgid "By"
+msgstr "Iki"
+
+msgid "Copyright"
+msgstr "Autorių teisės"
+
+msgid "Fullscreen mode"
+msgstr "Pilno ekrano režimas"
+
+msgid "Open an issue"
+msgstr "Atidarykite problemą"
+
+msgid "previous page"
+msgstr "Ankstesnis puslapis"
+
+msgid "Download this page"
+msgstr "Atsisiųskite šį puslapį"
+
+msgid "Theme by the"
+msgstr "Tema"
diff --git a/2023.2/_static/locales/lv/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/lv/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..74aa4d8985
Binary files /dev/null and b/2023.2/_static/locales/lv/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/lv/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/lv/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..c9633b545c
--- /dev/null
+++ b/2023.2/_static/locales/lv/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: lv\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "ieteikt rediģēt"
+
+msgid "Last updated on"
+msgstr "Pēdējoreiz atjaunināts"
+
+msgid "Edit this page"
+msgstr "Rediģēt šo lapu"
+
+msgid "Launch"
+msgstr "Uzsākt"
+
+msgid "Print to PDF"
+msgstr "Drukāt PDF formātā"
+
+msgid "open issue"
+msgstr "atklāts jautājums"
+
+msgid "Download notebook file"
+msgstr "Lejupielādēt piezīmju grāmatiņu"
+
+msgid "Toggle navigation"
+msgstr "Pārslēgt navigāciju"
+
+msgid "Source repository"
+msgstr "Avota krātuve"
+
+msgid "By the"
+msgstr "Ar"
+
+msgid "next page"
+msgstr "nākamā lapaspuse"
+
+msgid "repository"
+msgstr "krātuve"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinksa grāmatas tēma"
+
+msgid "Download source file"
+msgstr "Lejupielādēt avota failu"
+
+msgid "Contents"
+msgstr "Saturs"
+
+msgid "By"
+msgstr "Autors"
+
+msgid "Copyright"
+msgstr "Autortiesības"
+
+msgid "Fullscreen mode"
+msgstr "Pilnekrāna režīms"
+
+msgid "Open an issue"
+msgstr "Atveriet problēmu"
+
+msgid "previous page"
+msgstr "iepriekšējā lapa"
+
+msgid "Download this page"
+msgstr "Lejupielādējiet šo lapu"
+
+msgid "Theme by the"
+msgstr "Autora tēma"
diff --git a/2023.2/_static/locales/ml/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ml/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..2736e8fcf6
Binary files /dev/null and b/2023.2/_static/locales/ml/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ml/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ml/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..9a6a41e8ec
--- /dev/null
+++ b/2023.2/_static/locales/ml/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ml\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക"
+
+msgid "Last updated on"
+msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്"
+
+msgid "Edit this page"
+msgstr "ഈ പേജ് എഡിറ്റുചെയ്യുക"
+
+msgid "Launch"
+msgstr "സമാരംഭിക്കുക"
+
+msgid "Print to PDF"
+msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക"
+
+msgid "open issue"
+msgstr "തുറന്ന പ്രശ്നം"
+
+msgid "Download notebook file"
+msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക"
+
+msgid "Toggle navigation"
+msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക"
+
+msgid "Source repository"
+msgstr "ഉറവിട ശേഖരം"
+
+msgid "By the"
+msgstr "എഴുതിയത്"
+
+msgid "next page"
+msgstr "അടുത്ത പേജ്"
+
+msgid "Sphinx Book Theme"
+msgstr "സ്ഫിങ്ക്സ് പുസ്തക തീം"
+
+msgid "Download source file"
+msgstr "ഉറവിട ഫയൽ ഡൗൺലോഡുചെയ്യുക"
+
+msgid "By"
+msgstr "എഴുതിയത്"
+
+msgid "Copyright"
+msgstr "പകർപ്പവകാശം"
+
+msgid "Open an issue"
+msgstr "ഒരു പ്രശ്നം തുറക്കുക"
+
+msgid "previous page"
+msgstr "മുൻപത്തെ താൾ"
+
+msgid "Download this page"
+msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക"
+
+msgid "Theme by the"
+msgstr "പ്രമേയം"
diff --git a/2023.2/_static/locales/mr/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/mr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..fe530100d7
Binary files /dev/null and b/2023.2/_static/locales/mr/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/mr/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/mr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..ef72d8c6bc
--- /dev/null
+++ b/2023.2/_static/locales/mr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: mr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "संपादन सुचवा"
+
+msgid "Last updated on"
+msgstr "अखेरचे अद्यतनित"
+
+msgid "Edit this page"
+msgstr "हे पृष्ठ संपादित करा"
+
+msgid "Launch"
+msgstr "लाँच करा"
+
+msgid "Print to PDF"
+msgstr "पीडीएफवर मुद्रित करा"
+
+msgid "open issue"
+msgstr "खुला मुद्दा"
+
+msgid "Download notebook file"
+msgstr "नोटबुक फाईल डाउनलोड करा"
+
+msgid "Toggle navigation"
+msgstr "नेव्हिगेशन टॉगल करा"
+
+msgid "Source repository"
+msgstr "स्त्रोत भांडार"
+
+msgid "By the"
+msgstr "द्वारा"
+
+msgid "next page"
+msgstr "पुढील पृष्ठ"
+
+msgid "Sphinx Book Theme"
+msgstr "स्फिंक्स बुक थीम"
+
+msgid "Download source file"
+msgstr "स्त्रोत फाइल डाउनलोड करा"
+
+msgid "By"
+msgstr "द्वारा"
+
+msgid "Copyright"
+msgstr "कॉपीराइट"
+
+msgid "Open an issue"
+msgstr "एक मुद्दा उघडा"
+
+msgid "previous page"
+msgstr "मागील पान"
+
+msgid "Download this page"
+msgstr "हे पृष्ठ डाउनलोड करा"
+
+msgid "Theme by the"
+msgstr "द्वारा थीम"
diff --git a/2023.2/_static/locales/ms/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ms/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..f02603fa25
Binary files /dev/null and b/2023.2/_static/locales/ms/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ms/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ms/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..e29cbe2ec2
--- /dev/null
+++ b/2023.2/_static/locales/ms/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ms\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "cadangkan edit"
+
+msgid "Last updated on"
+msgstr "Terakhir dikemas kini pada"
+
+msgid "Edit this page"
+msgstr "Edit halaman ini"
+
+msgid "Launch"
+msgstr "Lancarkan"
+
+msgid "Print to PDF"
+msgstr "Cetak ke PDF"
+
+msgid "open issue"
+msgstr "isu terbuka"
+
+msgid "Download notebook file"
+msgstr "Muat turun fail buku nota"
+
+msgid "Toggle navigation"
+msgstr "Togol navigasi"
+
+msgid "Source repository"
+msgstr "Repositori sumber"
+
+msgid "By the"
+msgstr "Oleh"
+
+msgid "next page"
+msgstr "muka surat seterusnya"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema Buku Sphinx"
+
+msgid "Download source file"
+msgstr "Muat turun fail sumber"
+
+msgid "By"
+msgstr "Oleh"
+
+msgid "Copyright"
+msgstr "hak cipta"
+
+msgid "Open an issue"
+msgstr "Buka masalah"
+
+msgid "previous page"
+msgstr "halaman sebelumnya"
+
+msgid "Download this page"
+msgstr "Muat turun halaman ini"
+
+msgid "Theme by the"
+msgstr "Tema oleh"
diff --git a/2023.2/_static/locales/nl/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/nl/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..e59e7ecb30
Binary files /dev/null and b/2023.2/_static/locales/nl/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/nl/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/nl/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..e4844d7c98
--- /dev/null
+++ b/2023.2/_static/locales/nl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: nl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "suggereren bewerken"
+
+msgid "Last updated on"
+msgstr "Laatst geupdate op"
+
+msgid "Edit this page"
+msgstr "bewerk deze pagina"
+
+msgid "Launch"
+msgstr "Lancering"
+
+msgid "Print to PDF"
+msgstr "Afdrukken naar pdf"
+
+msgid "open issue"
+msgstr "open probleem"
+
+msgid "Download notebook file"
+msgstr "Download notebookbestand"
+
+msgid "Toggle navigation"
+msgstr "Schakel navigatie"
+
+msgid "Source repository"
+msgstr "Bronopslagplaats"
+
+msgid "By the"
+msgstr "Door de"
+
+msgid "next page"
+msgstr "volgende bladzijde"
+
+msgid "repository"
+msgstr "repository"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx-boekthema"
+
+msgid "Download source file"
+msgstr "Download het bronbestand"
+
+msgid "Contents"
+msgstr "Inhoud"
+
+msgid "By"
+msgstr "Door"
+
+msgid "Copyright"
+msgstr "auteursrechten"
+
+msgid "Fullscreen mode"
+msgstr "Volledig scherm"
+
+msgid "Open an issue"
+msgstr "Open een probleem"
+
+msgid "previous page"
+msgstr "vorige pagina"
+
+msgid "Download this page"
+msgstr "Download deze pagina"
+
+msgid "Theme by the"
+msgstr "Thema door de"
diff --git a/2023.2/_static/locales/no/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/no/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..6cd15c88de
Binary files /dev/null and b/2023.2/_static/locales/no/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/no/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/no/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..d079dd9b09
--- /dev/null
+++ b/2023.2/_static/locales/no/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: no\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "foreslå redigering"
+
+msgid "Last updated on"
+msgstr "Sist oppdatert den"
+
+msgid "Edit this page"
+msgstr "Rediger denne siden"
+
+msgid "Launch"
+msgstr "Start"
+
+msgid "Print to PDF"
+msgstr "Skriv ut til PDF"
+
+msgid "open issue"
+msgstr "åpent nummer"
+
+msgid "Download notebook file"
+msgstr "Last ned notatbokfilen"
+
+msgid "Toggle navigation"
+msgstr "Bytt navigasjon"
+
+msgid "Source repository"
+msgstr "Kildedepot"
+
+msgid "By the"
+msgstr "Ved"
+
+msgid "next page"
+msgstr "neste side"
+
+msgid "repository"
+msgstr "oppbevaringssted"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx boktema"
+
+msgid "Download source file"
+msgstr "Last ned kildefilen"
+
+msgid "Contents"
+msgstr "Innhold"
+
+msgid "By"
+msgstr "Av"
+
+msgid "Copyright"
+msgstr "opphavsrett"
+
+msgid "Fullscreen mode"
+msgstr "Fullskjerm-modus"
+
+msgid "Open an issue"
+msgstr "Åpne et problem"
+
+msgid "previous page"
+msgstr "forrige side"
+
+msgid "Download this page"
+msgstr "Last ned denne siden"
+
+msgid "Theme by the"
+msgstr "Tema av"
diff --git a/2023.2/_static/locales/pl/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/pl/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..9ebb584f77
Binary files /dev/null and b/2023.2/_static/locales/pl/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/pl/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/pl/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..fcac51d329
--- /dev/null
+++ b/2023.2/_static/locales/pl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: pl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "zaproponuj edycję"
+
+msgid "Last updated on"
+msgstr "Ostatnia aktualizacja"
+
+msgid "Edit this page"
+msgstr "Edytuj tę strone"
+
+msgid "Launch"
+msgstr "Uruchomić"
+
+msgid "Print to PDF"
+msgstr "Drukuj do PDF"
+
+msgid "open issue"
+msgstr "otwarty problem"
+
+msgid "Download notebook file"
+msgstr "Pobierz plik notatnika"
+
+msgid "Toggle navigation"
+msgstr "Przełącz nawigację"
+
+msgid "Source repository"
+msgstr "Repozytorium źródłowe"
+
+msgid "By the"
+msgstr "Przez"
+
+msgid "next page"
+msgstr "Następna strona"
+
+msgid "repository"
+msgstr "magazyn"
+
+msgid "Sphinx Book Theme"
+msgstr "Motyw książki Sphinx"
+
+msgid "Download source file"
+msgstr "Pobierz plik źródłowy"
+
+msgid "Contents"
+msgstr "Zawartość"
+
+msgid "By"
+msgstr "Przez"
+
+msgid "Copyright"
+msgstr "prawa autorskie"
+
+msgid "Fullscreen mode"
+msgstr "Pełny ekran"
+
+msgid "Open an issue"
+msgstr "Otwórz problem"
+
+msgid "previous page"
+msgstr "Poprzednia strona"
+
+msgid "Download this page"
+msgstr "Pobierz tę stronę"
+
+msgid "Theme by the"
+msgstr "Motyw autorstwa"
diff --git a/2023.2/_static/locales/pt/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/pt/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..d0ddb8728e
Binary files /dev/null and b/2023.2/_static/locales/pt/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/pt/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/pt/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..1761db08ae
--- /dev/null
+++ b/2023.2/_static/locales/pt/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: pt\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "sugerir edição"
+
+msgid "Last updated on"
+msgstr "Última atualização em"
+
+msgid "Edit this page"
+msgstr "Edite essa página"
+
+msgid "Launch"
+msgstr "Lançamento"
+
+msgid "Print to PDF"
+msgstr "Imprimir em PDF"
+
+msgid "open issue"
+msgstr "questão aberta"
+
+msgid "Download notebook file"
+msgstr "Baixar arquivo de notebook"
+
+msgid "Toggle navigation"
+msgstr "Alternar de navegação"
+
+msgid "Source repository"
+msgstr "Repositório fonte"
+
+msgid "By the"
+msgstr "Pelo"
+
+msgid "next page"
+msgstr "próxima página"
+
+msgid "repository"
+msgstr "repositório"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema do livro Sphinx"
+
+msgid "Download source file"
+msgstr "Baixar arquivo fonte"
+
+msgid "Contents"
+msgstr "Conteúdo"
+
+msgid "By"
+msgstr "De"
+
+msgid "Copyright"
+msgstr "direito autoral"
+
+msgid "Fullscreen mode"
+msgstr "Modo tela cheia"
+
+msgid "Open an issue"
+msgstr "Abra um problema"
+
+msgid "previous page"
+msgstr "página anterior"
+
+msgid "Download this page"
+msgstr "Baixe esta página"
+
+msgid "Theme by the"
+msgstr "Tema por"
diff --git a/2023.2/_static/locales/ro/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ro/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..3c36ab1df7
Binary files /dev/null and b/2023.2/_static/locales/ro/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ro/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ro/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..db865c8f65
--- /dev/null
+++ b/2023.2/_static/locales/ro/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ro\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "sugerează editare"
+
+msgid "Last updated on"
+msgstr "Ultima actualizare la"
+
+msgid "Edit this page"
+msgstr "Editați această pagină"
+
+msgid "Launch"
+msgstr "Lansa"
+
+msgid "Print to PDF"
+msgstr "Imprimați în PDF"
+
+msgid "open issue"
+msgstr "problema deschisă"
+
+msgid "Download notebook file"
+msgstr "Descărcați fișierul notebook"
+
+msgid "Toggle navigation"
+msgstr "Comutare navigare"
+
+msgid "Source repository"
+msgstr "Depozit sursă"
+
+msgid "By the"
+msgstr "Langa"
+
+msgid "next page"
+msgstr "pagina următoare"
+
+msgid "repository"
+msgstr "repertoriu"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema Sphinx Book"
+
+msgid "Download source file"
+msgstr "Descărcați fișierul sursă"
+
+msgid "Contents"
+msgstr "Cuprins"
+
+msgid "By"
+msgstr "De"
+
+msgid "Copyright"
+msgstr "Drepturi de autor"
+
+msgid "Fullscreen mode"
+msgstr "Modul ecran întreg"
+
+msgid "Open an issue"
+msgstr "Deschideți o problemă"
+
+msgid "previous page"
+msgstr "pagina anterioară"
+
+msgid "Download this page"
+msgstr "Descarcă această pagină"
+
+msgid "Theme by the"
+msgstr "Tema de"
diff --git a/2023.2/_static/locales/ru/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ru/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..6b8ca41f36
Binary files /dev/null and b/2023.2/_static/locales/ru/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ru/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ru/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..84ab6eb531
--- /dev/null
+++ b/2023.2/_static/locales/ru/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ru\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "предложить редактировать"
+
+msgid "Last updated on"
+msgstr "Последнее обновление"
+
+msgid "Edit this page"
+msgstr "Редактировать эту страницу"
+
+msgid "Launch"
+msgstr "Запуск"
+
+msgid "Print to PDF"
+msgstr "Распечатать в PDF"
+
+msgid "open issue"
+msgstr "открытый вопрос"
+
+msgid "Download notebook file"
+msgstr "Скачать файл записной книжки"
+
+msgid "Toggle navigation"
+msgstr "Переключить навигацию"
+
+msgid "Source repository"
+msgstr "Исходный репозиторий"
+
+msgid "By the"
+msgstr "Посредством"
+
+msgid "next page"
+msgstr "Следующая страница"
+
+msgid "repository"
+msgstr "хранилище"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема книги Сфинкс"
+
+msgid "Download source file"
+msgstr "Скачать исходный файл"
+
+msgid "Contents"
+msgstr "Содержание"
+
+msgid "By"
+msgstr "По"
+
+msgid "Copyright"
+msgstr "авторское право"
+
+msgid "Fullscreen mode"
+msgstr "Полноэкранный режим"
+
+msgid "Open an issue"
+msgstr "Открыть вопрос"
+
+msgid "previous page"
+msgstr "Предыдущая страница"
+
+msgid "Download this page"
+msgstr "Загрузите эту страницу"
+
+msgid "Theme by the"
+msgstr "Тема от"
diff --git a/2023.2/_static/locales/sk/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/sk/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..59bd0ddfa3
Binary files /dev/null and b/2023.2/_static/locales/sk/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/sk/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/sk/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..e44878b50e
--- /dev/null
+++ b/2023.2/_static/locales/sk/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sk\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "navrhnúť úpravu"
+
+msgid "Last updated on"
+msgstr "Posledná aktualizácia dňa"
+
+msgid "Edit this page"
+msgstr "Upraviť túto stránku"
+
+msgid "Launch"
+msgstr "Spustiť"
+
+msgid "Print to PDF"
+msgstr "Tlač do PDF"
+
+msgid "open issue"
+msgstr "otvorené vydanie"
+
+msgid "Download notebook file"
+msgstr "Stiahnite si zošit"
+
+msgid "Toggle navigation"
+msgstr "Prepnúť navigáciu"
+
+msgid "Source repository"
+msgstr "Zdrojové úložisko"
+
+msgid "By the"
+msgstr "Podľa"
+
+msgid "next page"
+msgstr "ďalšia strana"
+
+msgid "repository"
+msgstr "Úložisko"
+
+msgid "Sphinx Book Theme"
+msgstr "Téma knihy Sfinga"
+
+msgid "Download source file"
+msgstr "Stiahnite si zdrojový súbor"
+
+msgid "Contents"
+msgstr "Obsah"
+
+msgid "By"
+msgstr "Autor:"
+
+msgid "Copyright"
+msgstr "Autorské práva"
+
+msgid "Fullscreen mode"
+msgstr "Režim celej obrazovky"
+
+msgid "Open an issue"
+msgstr "Otvorte problém"
+
+msgid "previous page"
+msgstr "predchádzajúca strana"
+
+msgid "Download this page"
+msgstr "Stiahnite si túto stránku"
+
+msgid "Theme by the"
+msgstr "Téma od"
diff --git a/2023.2/_static/locales/sl/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/sl/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..87bf26de68
Binary files /dev/null and b/2023.2/_static/locales/sl/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/sl/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/sl/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..228939bcdd
--- /dev/null
+++ b/2023.2/_static/locales/sl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "predlagajte urejanje"
+
+msgid "Last updated on"
+msgstr "Nazadnje posodobljeno dne"
+
+msgid "Edit this page"
+msgstr "Uredite to stran"
+
+msgid "Launch"
+msgstr "Kosilo"
+
+msgid "Print to PDF"
+msgstr "Natisni v PDF"
+
+msgid "open issue"
+msgstr "odprto vprašanje"
+
+msgid "Download notebook file"
+msgstr "Prenesite datoteko zvezka"
+
+msgid "Toggle navigation"
+msgstr "Preklopi navigacijo"
+
+msgid "Source repository"
+msgstr "Izvorno skladišče"
+
+msgid "By the"
+msgstr "Avtor"
+
+msgid "next page"
+msgstr "Naslednja stran"
+
+msgid "repository"
+msgstr "odlagališče"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema knjige Sphinx"
+
+msgid "Download source file"
+msgstr "Prenesite izvorno datoteko"
+
+msgid "Contents"
+msgstr "Vsebina"
+
+msgid "By"
+msgstr "Avtor"
+
+msgid "Copyright"
+msgstr "avtorske pravice"
+
+msgid "Fullscreen mode"
+msgstr "Celozaslonski način"
+
+msgid "Open an issue"
+msgstr "Odprite številko"
+
+msgid "previous page"
+msgstr "Prejšnja stran"
+
+msgid "Download this page"
+msgstr "Prenesite to stran"
+
+msgid "Theme by the"
+msgstr "Tema avtorja"
diff --git a/2023.2/_static/locales/sr/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/sr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..ec740f4852
Binary files /dev/null and b/2023.2/_static/locales/sr/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/sr/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/sr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..1a712a18d8
--- /dev/null
+++ b/2023.2/_static/locales/sr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "предложи уређивање"
+
+msgid "Last updated on"
+msgstr "Последње ажурирање"
+
+msgid "Edit this page"
+msgstr "Уредите ову страницу"
+
+msgid "Launch"
+msgstr "Лансирање"
+
+msgid "Print to PDF"
+msgstr "Испис у ПДФ"
+
+msgid "open issue"
+msgstr "отворено издање"
+
+msgid "Download notebook file"
+msgstr "Преузмите датотеку бележнице"
+
+msgid "Toggle navigation"
+msgstr "Укључи / искључи навигацију"
+
+msgid "Source repository"
+msgstr "Изворно спремиште"
+
+msgid "By the"
+msgstr "Од"
+
+msgid "next page"
+msgstr "Следећа страна"
+
+msgid "repository"
+msgstr "спремиште"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема књиге Спхинк"
+
+msgid "Download source file"
+msgstr "Преузми изворну датотеку"
+
+msgid "Contents"
+msgstr "Садржај"
+
+msgid "By"
+msgstr "Од стране"
+
+msgid "Copyright"
+msgstr "Ауторско право"
+
+msgid "Fullscreen mode"
+msgstr "Режим целог екрана"
+
+msgid "Open an issue"
+msgstr "Отворите издање"
+
+msgid "previous page"
+msgstr "Претходна страница"
+
+msgid "Download this page"
+msgstr "Преузмите ову страницу"
+
+msgid "Theme by the"
+msgstr "Тхеме би"
diff --git a/2023.2/_static/locales/sv/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/sv/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..be951bec20
Binary files /dev/null and b/2023.2/_static/locales/sv/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/sv/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/sv/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..7d2b56d949
--- /dev/null
+++ b/2023.2/_static/locales/sv/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sv\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "föreslå redigering"
+
+msgid "Last updated on"
+msgstr "Senast uppdaterad den"
+
+msgid "Edit this page"
+msgstr "Redigera den här sidan"
+
+msgid "Launch"
+msgstr "Lansera"
+
+msgid "Print to PDF"
+msgstr "Skriv ut till PDF"
+
+msgid "open issue"
+msgstr "öppet problem"
+
+msgid "Download notebook file"
+msgstr "Ladda ner anteckningsbokfilen"
+
+msgid "Toggle navigation"
+msgstr "Växla navigering"
+
+msgid "Source repository"
+msgstr "Källförvar"
+
+msgid "By the"
+msgstr "Vid"
+
+msgid "next page"
+msgstr "nästa sida"
+
+msgid "repository"
+msgstr "förvar"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx boktema"
+
+msgid "Download source file"
+msgstr "Ladda ner källfil"
+
+msgid "Contents"
+msgstr "Innehåll"
+
+msgid "By"
+msgstr "Förbi"
+
+msgid "Copyright"
+msgstr "upphovsrätt"
+
+msgid "Fullscreen mode"
+msgstr "Fullskärmsläge"
+
+msgid "Open an issue"
+msgstr "Öppna ett problem"
+
+msgid "previous page"
+msgstr "föregående sida"
+
+msgid "Download this page"
+msgstr "Ladda ner den här sidan"
+
+msgid "Theme by the"
+msgstr "Tema av"
diff --git a/2023.2/_static/locales/ta/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ta/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..29f52e1f6f
Binary files /dev/null and b/2023.2/_static/locales/ta/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ta/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ta/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..c75ffe192c
--- /dev/null
+++ b/2023.2/_static/locales/ta/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ta\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "திருத்த பரிந்துரைக்கவும்"
+
+msgid "Last updated on"
+msgstr "கடைசியாக புதுப்பிக்கப்பட்டது"
+
+msgid "Edit this page"
+msgstr "இந்தப் பக்கத்தைத் திருத்தவும்"
+
+msgid "Launch"
+msgstr "தொடங்க"
+
+msgid "Print to PDF"
+msgstr "PDF இல் அச்சிடுக"
+
+msgid "open issue"
+msgstr "திறந்த பிரச்சினை"
+
+msgid "Download notebook file"
+msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்"
+
+msgid "Toggle navigation"
+msgstr "வழிசெலுத்தலை நிலைமாற்று"
+
+msgid "Source repository"
+msgstr "மூல களஞ்சியம்"
+
+msgid "By the"
+msgstr "மூலம்"
+
+msgid "next page"
+msgstr "அடுத்த பக்கம்"
+
+msgid "Sphinx Book Theme"
+msgstr "ஸ்பிங்க்ஸ் புத்தக தீம்"
+
+msgid "Download source file"
+msgstr "மூல கோப்பைப் பதிவிறக்குக"
+
+msgid "By"
+msgstr "வழங்கியவர்"
+
+msgid "Copyright"
+msgstr "பதிப்புரிமை"
+
+msgid "Open an issue"
+msgstr "சிக்கலைத் திறக்கவும்"
+
+msgid "previous page"
+msgstr "முந்தைய பக்கம்"
+
+msgid "Download this page"
+msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்"
+
+msgid "Theme by the"
+msgstr "வழங்கிய தீம்"
diff --git a/2023.2/_static/locales/te/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/te/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..0a5f4b46ad
Binary files /dev/null and b/2023.2/_static/locales/te/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/te/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/te/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..2595c03590
--- /dev/null
+++ b/2023.2/_static/locales/te/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: te\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "సవరించమని సూచించండి"
+
+msgid "Last updated on"
+msgstr "చివరిగా నవీకరించబడింది"
+
+msgid "Edit this page"
+msgstr "ఈ పేజీని సవరించండి"
+
+msgid "Launch"
+msgstr "ప్రారంభించండి"
+
+msgid "Print to PDF"
+msgstr "PDF కి ముద్రించండి"
+
+msgid "open issue"
+msgstr "ఓపెన్ ఇష్యూ"
+
+msgid "Download notebook file"
+msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి"
+
+msgid "Toggle navigation"
+msgstr "నావిగేషన్‌ను టోగుల్ చేయండి"
+
+msgid "Source repository"
+msgstr "మూల రిపోజిటరీ"
+
+msgid "By the"
+msgstr "ద్వారా"
+
+msgid "next page"
+msgstr "తరువాతి పేజీ"
+
+msgid "Sphinx Book Theme"
+msgstr "సింహిక పుస్తక థీమ్"
+
+msgid "Download source file"
+msgstr "మూల ఫైల్‌ను డౌన్‌లోడ్ చేయండి"
+
+msgid "By"
+msgstr "ద్వారా"
+
+msgid "Copyright"
+msgstr "కాపీరైట్"
+
+msgid "Open an issue"
+msgstr "సమస్యను తెరవండి"
+
+msgid "previous page"
+msgstr "ముందు పేజి"
+
+msgid "Download this page"
+msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి"
+
+msgid "Theme by the"
+msgstr "ద్వారా థీమ్"
diff --git a/2023.2/_static/locales/tg/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/tg/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..b21c6c6340
Binary files /dev/null and b/2023.2/_static/locales/tg/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/tg/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/tg/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..73cd30ea97
--- /dev/null
+++ b/2023.2/_static/locales/tg/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: tg\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "пешниҳод вироиш"
+
+msgid "Last updated on"
+msgstr "Last навсозӣ дар"
+
+msgid "Edit this page"
+msgstr "Ин саҳифаро таҳрир кунед"
+
+msgid "Launch"
+msgstr "Оғоз"
+
+msgid "Print to PDF"
+msgstr "Чоп ба PDF"
+
+msgid "open issue"
+msgstr "барориши кушод"
+
+msgid "Download notebook file"
+msgstr "Файли дафтарро зеркашӣ кунед"
+
+msgid "Toggle navigation"
+msgstr "Гузаришро иваз кунед"
+
+msgid "Source repository"
+msgstr "Анбори манбаъ"
+
+msgid "By the"
+msgstr "Бо"
+
+msgid "next page"
+msgstr "саҳифаи оянда"
+
+msgid "repository"
+msgstr "анбор"
+
+msgid "Sphinx Book Theme"
+msgstr "Сфинкс Мавзӯи китоб"
+
+msgid "Download source file"
+msgstr "Файли манбаъро зеркашӣ кунед"
+
+msgid "Contents"
+msgstr "Мундариҷа"
+
+msgid "By"
+msgstr "Бо"
+
+msgid "Copyright"
+msgstr "Ҳуқуқи муаллиф"
+
+msgid "Fullscreen mode"
+msgstr "Ҳолати экрани пурра"
+
+msgid "Open an issue"
+msgstr "Масъаларо кушоед"
+
+msgid "previous page"
+msgstr "саҳифаи қаблӣ"
+
+msgid "Download this page"
+msgstr "Ин саҳифаро зеркашӣ кунед"
+
+msgid "Theme by the"
+msgstr "Мавзӯъи аз"
diff --git a/2023.2/_static/locales/th/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/th/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..abede98aa1
Binary files /dev/null and b/2023.2/_static/locales/th/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/th/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/th/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..0392b4ad39
--- /dev/null
+++ b/2023.2/_static/locales/th/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: th\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "แนะนำแก้ไข"
+
+msgid "Last updated on"
+msgstr "ปรับปรุงล่าสุดเมื่อ"
+
+msgid "Edit this page"
+msgstr "แก้ไขหน้านี้"
+
+msgid "Launch"
+msgstr "เปิด"
+
+msgid "Print to PDF"
+msgstr "พิมพ์เป็น PDF"
+
+msgid "open issue"
+msgstr "เปิดปัญหา"
+
+msgid "Download notebook file"
+msgstr "ดาวน์โหลดไฟล์สมุดบันทึก"
+
+msgid "Toggle navigation"
+msgstr "ไม่ต้องสลับช่องทาง"
+
+msgid "Source repository"
+msgstr "ที่เก็บซอร์ส"
+
+msgid "By the"
+msgstr "โดย"
+
+msgid "next page"
+msgstr "หน้าต่อไป"
+
+msgid "repository"
+msgstr "ที่เก็บ"
+
+msgid "Sphinx Book Theme"
+msgstr "ธีมหนังสือสฟิงซ์"
+
+msgid "Download source file"
+msgstr "ดาวน์โหลดไฟล์ต้นฉบับ"
+
+msgid "Contents"
+msgstr "สารบัญ"
+
+msgid "By"
+msgstr "โดย"
+
+msgid "Copyright"
+msgstr "ลิขสิทธิ์"
+
+msgid "Fullscreen mode"
+msgstr "โหมดเต็มหน้าจอ"
+
+msgid "Open an issue"
+msgstr "เปิดปัญหา"
+
+msgid "previous page"
+msgstr "หน้าที่แล้ว"
+
+msgid "Download this page"
+msgstr "ดาวน์โหลดหน้านี้"
+
+msgid "Theme by the"
+msgstr "ธีมโดย"
diff --git a/2023.2/_static/locales/tl/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/tl/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..8df1b73310
Binary files /dev/null and b/2023.2/_static/locales/tl/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/tl/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/tl/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..c8375b5431
--- /dev/null
+++ b/2023.2/_static/locales/tl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: tl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "iminumungkahi i-edit"
+
+msgid "Last updated on"
+msgstr "Huling na-update noong"
+
+msgid "Edit this page"
+msgstr "I-edit ang pahinang ito"
+
+msgid "Launch"
+msgstr "Ilunsad"
+
+msgid "Print to PDF"
+msgstr "I-print sa PDF"
+
+msgid "open issue"
+msgstr "bukas na isyu"
+
+msgid "Download notebook file"
+msgstr "Mag-download ng file ng notebook"
+
+msgid "Toggle navigation"
+msgstr "I-toggle ang pag-navigate"
+
+msgid "Source repository"
+msgstr "Pinagmulan ng imbakan"
+
+msgid "By the"
+msgstr "Sa pamamagitan ng"
+
+msgid "next page"
+msgstr "Susunod na pahina"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema ng Sphinx Book"
+
+msgid "Download source file"
+msgstr "Mag-download ng file ng pinagmulan"
+
+msgid "By"
+msgstr "Ni"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Open an issue"
+msgstr "Magbukas ng isyu"
+
+msgid "previous page"
+msgstr "Nakaraang pahina"
+
+msgid "Download this page"
+msgstr "I-download ang pahinang ito"
+
+msgid "Theme by the"
+msgstr "Tema ng"
diff --git a/2023.2/_static/locales/tr/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/tr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..029ae18afb
Binary files /dev/null and b/2023.2/_static/locales/tr/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/tr/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/tr/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..47d7bdf7f5
--- /dev/null
+++ b/2023.2/_static/locales/tr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: tr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "düzenleme öner"
+
+msgid "Last updated on"
+msgstr "Son güncelleme tarihi"
+
+msgid "Edit this page"
+msgstr "Bu sayfayı düzenle"
+
+msgid "Launch"
+msgstr "Başlatmak"
+
+msgid "Print to PDF"
+msgstr "PDF olarak yazdır"
+
+msgid "open issue"
+msgstr "Açık konu"
+
+msgid "Download notebook file"
+msgstr "Defter dosyasını indirin"
+
+msgid "Toggle navigation"
+msgstr "Gezinmeyi değiştir"
+
+msgid "Source repository"
+msgstr "Kaynak kod deposu"
+
+msgid "By the"
+msgstr "Tarafından"
+
+msgid "next page"
+msgstr "sonraki Sayfa"
+
+msgid "repository"
+msgstr "depo"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfenks Kitap Teması"
+
+msgid "Download source file"
+msgstr "Kaynak dosyayı indirin"
+
+msgid "Contents"
+msgstr "İçindekiler"
+
+msgid "By"
+msgstr "Tarafından"
+
+msgid "Copyright"
+msgstr "Telif hakkı"
+
+msgid "Fullscreen mode"
+msgstr "Tam ekran modu"
+
+msgid "Open an issue"
+msgstr "Bir sorunu açın"
+
+msgid "previous page"
+msgstr "önceki sayfa"
+
+msgid "Download this page"
+msgstr "Bu sayfayı indirin"
+
+msgid "Theme by the"
+msgstr "Tarafından tema"
diff --git a/2023.2/_static/locales/uk/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/uk/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..16ab78909c
Binary files /dev/null and b/2023.2/_static/locales/uk/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/uk/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/uk/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..e85f6f16ac
--- /dev/null
+++ b/2023.2/_static/locales/uk/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: uk\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "запропонувати редагувати"
+
+msgid "Last updated on"
+msgstr "Останнє оновлення:"
+
+msgid "Edit this page"
+msgstr "Редагувати цю сторінку"
+
+msgid "Launch"
+msgstr "Запуск"
+
+msgid "Print to PDF"
+msgstr "Друк у форматі PDF"
+
+msgid "open issue"
+msgstr "відкритий випуск"
+
+msgid "Download notebook file"
+msgstr "Завантажте файл блокнота"
+
+msgid "Toggle navigation"
+msgstr "Переключити навігацію"
+
+msgid "Source repository"
+msgstr "Джерело сховища"
+
+msgid "By the"
+msgstr "По"
+
+msgid "next page"
+msgstr "Наступна сторінка"
+
+msgid "repository"
+msgstr "сховище"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема книги \"Сфінкс\""
+
+msgid "Download source file"
+msgstr "Завантажити вихідний файл"
+
+msgid "Contents"
+msgstr "Зміст"
+
+msgid "By"
+msgstr "Автор"
+
+msgid "Copyright"
+msgstr "Авторське право"
+
+msgid "Fullscreen mode"
+msgstr "Повноекранний режим"
+
+msgid "Open an issue"
+msgstr "Відкрийте випуск"
+
+msgid "previous page"
+msgstr "Попередня сторінка"
+
+msgid "Download this page"
+msgstr "Завантажте цю сторінку"
+
+msgid "Theme by the"
+msgstr "Тема від"
diff --git a/2023.2/_static/locales/ur/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/ur/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..de8c84b935
Binary files /dev/null and b/2023.2/_static/locales/ur/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/ur/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/ur/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..0f90726c12
--- /dev/null
+++ b/2023.2/_static/locales/ur/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ur\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "ترمیم کی تجویز کریں"
+
+msgid "Last updated on"
+msgstr "آخری بار تازہ کاری ہوئی"
+
+msgid "Edit this page"
+msgstr "اس صفحے میں ترمیم کریں"
+
+msgid "Launch"
+msgstr "لانچ کریں"
+
+msgid "Print to PDF"
+msgstr "پی ڈی ایف پرنٹ کریں"
+
+msgid "open issue"
+msgstr "کھلا مسئلہ"
+
+msgid "Download notebook file"
+msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں"
+
+msgid "Toggle navigation"
+msgstr "نیویگیشن ٹوگل کریں"
+
+msgid "Source repository"
+msgstr "ماخذ ذخیرہ"
+
+msgid "By the"
+msgstr "کی طرف"
+
+msgid "next page"
+msgstr "اگلا صفحہ"
+
+msgid "Sphinx Book Theme"
+msgstr "سپنکس بک تھیم"
+
+msgid "Download source file"
+msgstr "سورس فائل ڈاؤن لوڈ کریں"
+
+msgid "By"
+msgstr "بذریعہ"
+
+msgid "Copyright"
+msgstr "کاپی رائٹ"
+
+msgid "Open an issue"
+msgstr "ایک مسئلہ کھولیں"
+
+msgid "previous page"
+msgstr "سابقہ ​​صفحہ"
+
+msgid "Download this page"
+msgstr "اس صفحے کو ڈاؤن لوڈ کریں"
+
+msgid "Theme by the"
+msgstr "کے ذریعہ تھیم"
diff --git a/2023.2/_static/locales/vi/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/vi/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..2bb32555c3
Binary files /dev/null and b/2023.2/_static/locales/vi/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/vi/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/vi/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..2cb5cf3b82
--- /dev/null
+++ b/2023.2/_static/locales/vi/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: vi\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "đề nghị chỉnh sửa"
+
+msgid "Last updated on"
+msgstr "Cập nhật lần cuối vào"
+
+msgid "Edit this page"
+msgstr "chỉnh sửa trang này"
+
+msgid "Launch"
+msgstr "Phóng"
+
+msgid "Print to PDF"
+msgstr "In sang PDF"
+
+msgid "open issue"
+msgstr "vấn đề mở"
+
+msgid "Download notebook file"
+msgstr "Tải xuống tệp sổ tay"
+
+msgid "Toggle navigation"
+msgstr "Chuyển đổi điều hướng thành"
+
+msgid "Source repository"
+msgstr "Kho nguồn"
+
+msgid "By the"
+msgstr "Bằng"
+
+msgid "next page"
+msgstr "Trang tiếp theo"
+
+msgid "repository"
+msgstr "kho"
+
+msgid "Sphinx Book Theme"
+msgstr "Chủ đề sách nhân sư"
+
+msgid "Download source file"
+msgstr "Tải xuống tệp nguồn"
+
+msgid "Contents"
+msgstr "Nội dung"
+
+msgid "By"
+msgstr "Bởi"
+
+msgid "Copyright"
+msgstr "Bản quyền"
+
+msgid "Fullscreen mode"
+msgstr "Chế độ toàn màn hình"
+
+msgid "Open an issue"
+msgstr "Mở một vấn đề"
+
+msgid "previous page"
+msgstr "trang trước"
+
+msgid "Download this page"
+msgstr "Tải xuống trang này"
+
+msgid "Theme by the"
+msgstr "Chủ đề của"
diff --git a/2023.2/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..0e3235d090
Binary files /dev/null and b/2023.2/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/zh_CN/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/zh_CN/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..f91f3ba0a2
--- /dev/null
+++ b/2023.2/_static/locales/zh_CN/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: zh_CN\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "提出修改建议"
+
+msgid "Last updated on"
+msgstr "上次更新时间："
+
+msgid "Edit this page"
+msgstr "编辑此页面"
+
+msgid "Launch"
+msgstr "启动"
+
+msgid "Print to PDF"
+msgstr "列印成 PDF"
+
+msgid "open issue"
+msgstr "创建议题"
+
+msgid "Download notebook file"
+msgstr "下载笔记本文件"
+
+msgid "Toggle navigation"
+msgstr "显示或隐藏导航栏"
+
+msgid "Source repository"
+msgstr "源码库"
+
+msgid "By the"
+msgstr "作者："
+
+msgid "next page"
+msgstr "下一页"
+
+msgid "repository"
+msgstr "仓库"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx Book 主题"
+
+msgid "Download source file"
+msgstr "下载源文件"
+
+msgid "Contents"
+msgstr "目录"
+
+msgid "By"
+msgstr "作者："
+
+msgid "Copyright"
+msgstr "版权"
+
+msgid "Fullscreen mode"
+msgstr "全屏模式"
+
+msgid "Open an issue"
+msgstr "创建议题"
+
+msgid "previous page"
+msgstr "上一页"
+
+msgid "Download this page"
+msgstr "下载此页面"
+
+msgid "Theme by the"
+msgstr "主题作者："
diff --git a/2023.2/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo b/2023.2/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000..9116fa95d0
Binary files /dev/null and b/2023.2/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo differ
diff --git a/2023.2/_static/locales/zh_TW/LC_MESSAGES/booktheme.po b/2023.2/_static/locales/zh_TW/LC_MESSAGES/booktheme.po
new file mode 100644
index 0000000000..7833d90432
--- /dev/null
+++ b/2023.2/_static/locales/zh_TW/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: zh_TW\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "suggest edit"
+msgstr "提出修改建議"
+
+msgid "Last updated on"
+msgstr "最後更新時間："
+
+msgid "Edit this page"
+msgstr "編輯此頁面"
+
+msgid "Launch"
+msgstr "啟動"
+
+msgid "Print to PDF"
+msgstr "列印成 PDF"
+
+msgid "open issue"
+msgstr "公開的問題"
+
+msgid "Download notebook file"
+msgstr "下載 Notebook 檔案"
+
+msgid "Toggle navigation"
+msgstr "顯示或隱藏導覽列"
+
+msgid "Source repository"
+msgstr "來源儲存庫"
+
+msgid "By the"
+msgstr "作者："
+
+msgid "next page"
+msgstr "下一頁"
+
+msgid "repository"
+msgstr "儲存庫"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx Book 佈景主題"
+
+msgid "Download source file"
+msgstr "下載原始檔"
+
+msgid "Contents"
+msgstr "目錄"
+
+msgid "By"
+msgstr "作者："
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Fullscreen mode"
+msgstr "全螢幕模式"
+
+msgid "Open an issue"
+msgstr "開啟議題"
+
+msgid "previous page"
+msgstr "上一頁"
+
+msgid "Download this page"
+msgstr "下載此頁面"
+
+msgid "Theme by the"
+msgstr "佈景主題作者："
diff --git a/2023.2/_static/nbsphinx-code-cells.css b/2023.2/_static/nbsphinx-code-cells.css
index 199fa5a42f..a3fb27c30f 100644
--- a/2023.2/_static/nbsphinx-code-cells.css
+++ b/2023.2/_static/nbsphinx-code-cells.css
@@ -62,12 +62,16 @@ div.nblast.container {
 }
 
 /* input prompt */
-div.nbinput.container div.prompt pre {
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
     color: #307FC1;
 }
 
 /* output prompt */
-div.nboutput.container div.prompt pre {
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
     color: #BF5B3D;
 }
 
@@ -204,8 +208,10 @@ div.nboutput.container div.output_area > div[class^='highlight']{
     overflow-y: hidden;
 }
 
-/* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */
-.prompt .copybtn {
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
     display: none;
 }
 
diff --git a/2023.2/_static/pygments.css b/2023.2/_static/pygments.css
index 691aeb82d0..0d49244eda 100644
--- a/2023.2/_static/pygments.css
+++ b/2023.2/_static/pygments.css
@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #FF0000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #00A000 } /* Generic.Inserted */
diff --git a/2023.2/_static/sbt-webpack-macros.html b/2023.2/_static/sbt-webpack-macros.html
new file mode 100644
index 0000000000..6cbf559faa
--- /dev/null
+++ b/2023.2/_static/sbt-webpack-macros.html
@@ -0,0 +1,11 @@
+<!--
+  All these macros are auto-generated and must **NOT** be edited by hand.
+  See the webpack.config.js file, to learn more about how this is generated.
+-->
+{% macro head_pre_bootstrap() %}
+  <link href="{{ pathto('_static/styles/sphinx-book-theme.css', 1) }}" rel="stylesheet">
+{% endmacro %}
+
+{% macro body_post() %}
+  <script src="{{ pathto('_static/scripts/sphinx-book-theme.js', 1) }}"></script>
+{% endmacro %}
diff --git a/2023.2/_static/scripts/bootstrap.js b/2023.2/_static/scripts/bootstrap.js
new file mode 100644
index 0000000000..bda8a60272
--- /dev/null
+++ b/2023.2/_static/scripts/bootstrap.js
@@ -0,0 +1,3 @@
+/*! For license information please see bootstrap.js.LICENSE.txt */
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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ye.findOne(Ze,this._indicatorsElement);e.classList.remove(Je),e.removeAttribute("aria-current");const i=ye.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Je),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===He,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>ue.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),Rt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Je),i.classList.remove(Je,c,l),this._isSliding=!1,r(qe)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ye.findOne(ei,this._element)}_getItems(){return ye.find(ti,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Yt()?t===We?Be:He:t===We?He:Be}_orderToDirection(t){return Yt()?t===Be?We:ze:t===Be?ze:We}static jQueryInterface(t){return this.each((function(){const e=oi.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}ue.on(document,Ue,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=ye.getElementFromSelector(this);if(!e||!e.classList.contains(Ge))return;t.preventDefault();const i=oi.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===ge.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),ue.on(window,Xe,(()=>{const t=ye.find('[data-bs-ride="carousel"]');for(const e of t)oi.getOrCreateInstance(e)})),Kt(oi);const ri=".bs.collapse",ai=`show${ri}`,li=`shown${ri}`,ci=`hide${ri}`,hi=`hidden${ri}`,di=`click${ri}.data-api`,ui="show",fi="collapse",pi="collapsing",mi=`:scope .${fi} .${fi}`,gi='[data-bs-toggle="collapse"]',_i={parent:null,toggle:!0},bi={parent:"(null|element)",toggle:"boolean"};class vi extends be{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=ye.find(gi);for(const t of i){const e=ye.getSelectorFromElement(t),i=ye.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return _i}static get DefaultType(){return bi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>vi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(ue.trigger(this._element,ai).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(fi),this._element.classList.add(pi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(pi),this._element.classList.add(fi,ui),this._element.style[e]="",ue.trigger(this._element,li)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(ue.trigger(this._element,ci).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,Rt(this._element),this._element.classList.add(pi),this._element.classList.remove(fi,ui);for(const t of this._triggerArray){const e=ye.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(pi),this._element.classList.add(fi),ue.trigger(this._element,hi)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(ui)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ft(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(gi);for(const e of t){const t=ye.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ye.find(mi,this._config.parent);return ye.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=vi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}ue.on(document,di,gi,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of ye.getMultipleElementsFromSelector(this))vi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Kt(vi);const yi="dropdown",wi=".bs.dropdown",Ei=".data-api",Ai="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ei}`,Si=`keydown${wi}${Ei}`,Di=`keyup${wi}${Ei}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=Yt()?"top-end":"top-start",ji=Yt()?"top-start":"top-end",Fi=Yt()?"bottom-end":"bottom-start",Hi=Yt()?"bottom-start":"bottom-end",Bi=Yt()?"left-start":"right-start",Wi=Yt()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends be{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ye.next(this._element,Pi)[0]||ye.prev(this._element,Pi)[0]||ye.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Bt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!ue.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),ue.trigger(this._element,ki,t)}}hide(){if(Bt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!ue.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),ge.removeDataAttribute(this._menu,"popper"),ue.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!jt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:jt(this._config.reference)?t=Ft(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=St(t,this._menu,i)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return Wi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(ge.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Qt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=ye.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Ht(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ye.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ai,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:ye.prev(this,Ii)[0]||ye.next(this,Ii)[0]||ye.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}ue.on(document,Si,Ii,qi.dataApiKeydownHandler),ue.on(document,Si,Pi,qi.dataApiKeydownHandler),ue.on(document,Li,qi.clearMenus),ue.on(document,Di,qi.clearMenus),ue.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),Kt(qi);const Vi="backdrop",Yi="show",Ki=`mousedown.bs.${Vi}`,Qi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Xi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends _e{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Qi}static get DefaultType(){return Xi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void Qt(t);this._append();const e=this._getElement();this._config.isAnimated&&Rt(e),e.classList.add(Yi),this._emulateAnimation((()=>{Qt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Yi),this._emulateAnimation((()=>{this.dispose(),Qt(t)}))):Qt(t)}dispose(){this._isAppended&&(ue.off(this._element,Ki),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ft(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),ue.on(t,Ki,(()=>{Qt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Xt(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends _e{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),ue.off(document,Gi),ue.on(document,Ji,(t=>this._handleFocusin(t))),ue.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,ue.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ye.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&ge.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=ge.getDataAttribute(t,e);null!==i?(ge.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(jt(t))e(t);else for(const i of ye.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",En="show",An="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends be{constructor(t,e){super(t,e),this._dialog=ye.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||ue.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(ue.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){ue.off(window,hn),ue.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ye.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),Rt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,ue.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){ue.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),ue.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),ue.on(this._element,bn,(t=>{ue.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),ue.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(ue.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(An)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(An),this._queueCallback((()=>{this._element.classList.remove(An),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Yt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Yt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}ue.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=ye.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),ue.one(e,pn,(t=>{t.defaultPrevented||ue.one(e,fn,(()=>{Ht(this)&&this.focus()}))}));const i=ye.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),we(On),Kt(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Bn=`click${xn}${kn}`,Wn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends be{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||ue.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),ue.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(ue.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),ue.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():ue.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){ue.on(this._element,Wn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():ue.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}ue.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=ye.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Bt(this))return;ue.one(e,Fn,(()=>{Ht(this)&&this.focus()}));const i=ye.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),ue.on(window,Ln,(()=>{for(const t of ye.find(In))qn.getOrCreateInstance(t).show()})),ue.on(window,Hn,(()=>{for(const t of ye.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),we(qn),Kt(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Yn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Kn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Qn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Yn.has(i)||Boolean(Kn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Xn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends _e{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Xn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=ye.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?jt(e)?this._putElementInTemplate(Ft(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Qn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Qt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:Yt()?"left":"right",BOTTOM:"bottom",LEFT:Yt()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends be{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),ue.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=ue.trigger(this._element,this.constructor.eventName("show")),e=(Wt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),ue.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._queueCallback((()=>{ue.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!ue.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),ue.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=Qt(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return St(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Qt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Qt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)ue.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");ue.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),ue.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},ue.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=ge.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ft(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Kt(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Kt(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Es={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class As extends be{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return ws}static get DefaultType(){return Es}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ft(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(ue.off(this._config.target,ms),ue.on(this._config.target,ms,bs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ye.find(bs,this._config.target);for(const e of t){if(!e.hash||Bt(e))continue;const t=ye.findOne(decodeURI(e.hash),this._element);Ht(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),ue.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ye.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of ye.parents(t,".nav, .list-group"))for(const t of ye.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=ye.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=As.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}ue.on(window,gs,(()=>{for(const t of ye.find('[data-bs-spy="scroll"]'))As.getOrCreateInstance(t)})),Kt(As);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Bs="show",Ws=".dropdown-toggle",zs=`:not(${Ws})`,Rs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${zs}, .list-group-item${zs}, [role="tab"]${zs}, ${Rs}`,Vs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Ys extends be{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),ue.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?ue.trigger(e,Cs,{relatedTarget:t}):null;ue.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(ye.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),ue.trigger(t,ks,{relatedTarget:e})):t.classList.add(Bs)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(ye.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),ue.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Bs)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Bt(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=Ut(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Ys.getOrCreateInstance(i).show())}_getChildren(){return ye.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=ye.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=ye.findOne(t,i);s&&s.classList.toggle(n,e)};n(Ws,Fs),n(".dropdown-menu",Bs),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(qs)?t:ye.findOne(qs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Ys.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}ue.on(document,Ls,Rs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Bt(this)||Ys.getOrCreateInstance(this).show()})),ue.on(window,Ds,(()=>{for(const t of ye.find(Vs))Ys.getOrCreateInstance(t)})),Kt(Ys);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Us=`focusin${Ks}`,Gs=`focusout${Ks}`,Js=`hide${Ks}`,Zs=`hidden${Ks}`,to=`show${Ks}`,eo=`shown${Ks}`,io="hide",no="show",so="showing",oo={animation:"boolean",autohide:"boolean",delay:"number"},ro={animation:!0,autohide:!0,delay:5e3};class ao extends be{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return ro}static get DefaultType(){return oo}static get 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+//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/2023.2/_static/scripts/bootstrap.js.LICENSE.txt b/2023.2/_static/scripts/bootstrap.js.LICENSE.txt
new file mode 100644
index 0000000000..10f979d078
--- /dev/null
+++ b/2023.2/_static/scripts/bootstrap.js.LICENSE.txt
@@ -0,0 +1,5 @@
+/*!
+  * Bootstrap v5.3.2 (https://getbootstrap.com/)
+  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
diff --git a/2023.2/_static/scripts/bootstrap.js.map b/2023.2/_static/scripts/bootstrap.js.map
new file mode 100644
index 0000000000..e5bc15752d
--- /dev/null
+++ b/2023.2/_static/scripts/bootstrap.js.map
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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.3.2 (https://getbootstrap.com/)\n  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n    const instanceMap = elementMap.get(element);\n\n    // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n    instanceMap.set(key, instance);\n  },\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n    return null;\n  },\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key);\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n  }\n  return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n  return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  }\n\n  // Get transition-duration of the element\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n  return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object));\n  }\n  return null;\n};\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])');\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n    if (summary === null) {\n      return false;\n    }\n  }\n  return elementIsVisible;\n};\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n  if (element instanceof ShadowRoot) {\n    return element;\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null;\n  }\n  return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n  return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement);\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n  index += shouldGetNext ? 1 : -1;\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n    return fn.apply(element, [event]);\n  };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string';\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n  return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n    callable = wrapFunction(callable);\n  }\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n  if (!fn) {\n    return;\n  }\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n    const evt = hydrateObj(new Event(event, {\n      bubbles,\n      cancelable: true\n    }), args);\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n    return evt;\n  }\n};\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value;\n        }\n      });\n    }\n  }\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n  if (value === 'false') {\n    return false;\n  }\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n  if (value === '' || value === 'null') {\n    return null;\n  }\n  if (typeof value !== 'string') {\n    return value;\n  }\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n    return attributes;\n  },\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n  static get DefaultType() {\n    return {};\n  }\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    return config;\n  }\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.2';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n    if (!element) {\n      return;\n    }\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n  static get VERSION() {\n    return VERSION;\n  }\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href');\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n    selector = hrefAttribute && hrefAttribute !== '#' ? parseSelector(hrefAttribute.trim()) : null;\n  }\n  return selector;\n};\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n    return parents;\n  },\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n      previous = previous.previousElementSibling;\n    }\n    return [];\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n      next = next.nextElementSibling;\n    }\n    return [];\n  },\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  },\n  getSelectorFromElement(element) {\n    const selector = getSelector(element);\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null;\n    }\n    return null;\n  },\n  getElementFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.findOne(selector) : null;\n  },\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.find(selector) : [];\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n    if (isDisabled(this)) {\n      return;\n    }\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target);\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove();\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n    this._initEvents();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$c;\n  }\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n  static get NAME() {\n    return NAME$d;\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n    this._handleSwipe();\n    execute(this._config.endCallback);\n  }\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n    if (!direction) {\n      return;\n    }\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n    this._addEventListeners();\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$b;\n  }\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n  static get NAME() {\n    return NAME$c;\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n    this._clearInterval();\n  }\n  cycle() {\n    this._clearInterval();\n    this._updateInterval();\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n    this.cycle();\n  }\n  to(index) {\n    const items = this._getItems();\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n    const activeIndex = this._getItemIndex(this._getActive());\n    if (activeIndex === index) {\n      return;\n    }\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n    this._slide(order, items[index]);\n  }\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause();\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n    const direction = KEY_TO_DIRECTION[event.key];\n    if (direction) {\n      event.preventDefault();\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n    if (!element) {\n      return;\n    }\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n    const activeElement = this._getActive();\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n    if (nextElement === activeElement) {\n      return;\n    }\n    const nextElementIndex = this._getItemIndex(nextElement);\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return;\n    }\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n    this._setActiveIndicatorElement(nextElementIndex);\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n  if (slideIndex) {\n    carousel.to(slideIndex);\n    carousel._maybeEnableCycle();\n    return;\n  }\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n    carousel._maybeEnableCycle();\n    return;\n  }\n  carousel.prev();\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n    this._initializeChildren();\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$a;\n  }\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n  static get NAME() {\n    return NAME$b;\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n    let activeChildren = [];\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n    const dimension = this._getDimension();\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.style[dimension] = 0;\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n    this._queueCallback(complete, this._element, true);\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    const dimension = this._getDimension();\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger);\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n    this._element.style[dimension] = '';\n    this._queueCallback(complete, this._element, true);\n  }\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n    config.parent = getElement(config.parent);\n    return config;\n  }\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element);\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {};\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$9;\n  }\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n  static get NAME() {\n    return NAME$a;\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._createPopper();\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    this._element.focus();\n    this._element.setAttribute('aria-expanded', true);\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    this._completeHide(relatedTarget);\n  }\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    super.dispose();\n  }\n  update() {\n    this._inNavbar = this._detectNavbar();\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.setAttribute('aria-expanded', 'false');\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n  _getConfig(config) {\n    config = super._getConfig(config);\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n    return config;\n  }\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n    let referenceElement = this._element;\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n    const popperConfig = this._getPopperConfig();\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n  _getPlacement() {\n    const parentDropdown = this._parent;\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    };\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n    if (!items.length) {\n      return;\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n      context._completeHide(relatedTarget);\n    }\n  }\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n    event.preventDefault();\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n      instance._selectMenuItem(event);\n      return;\n    }\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n};\n\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$8;\n  }\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n  static get NAME() {\n    return NAME$9;\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._append();\n    const element = this._getElement();\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n    element.classList.add(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n    this._element.remove();\n    this._isAppended = false;\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n      this._element = backdrop;\n    }\n    return this._element;\n  }\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n    const element = this._getElement();\n    this._config.rootElement.append(element);\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n};\n\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$7;\n  }\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n  static get NAME() {\n    return NAME$8;\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n    const elements = SelectorEngine.focusableChildren(trapElement);\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n  hide() {\n    const width = this.getWidth();\n    this._disableOverFlow();\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n  isOverflowing() {\n    return this.getWidth() > 0;\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n    this._element.style.overflow = 'hidden';\n  }\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n      this._saveInitialAttribute(element, styleProperty);\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty);\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$6;\n  }\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n  static get NAME() {\n    return NAME$7;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._isTransitioning = true;\n    this._scrollBar.hide();\n    document.body.classList.add(CLASS_NAME_OPEN);\n    this._adjustDialog();\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = false;\n    this._isTransitioning = true;\n    this._focustrap.deactivate();\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n  dispose() {\n    EventHandler.off(window, EVENT_KEY$4);\n    EventHandler.off(this._dialog, EVENT_KEY$4);\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n  handleUpdate() {\n    this._adjustDialog();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n    this._element.style.display = 'block';\n    this._element.removeAttribute('aria-hidden');\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n          return;\n        }\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n  _hideModal() {\n    this._element.style.display = 'none';\n    this._element.setAttribute('aria-hidden', true);\n    this._element.removeAttribute('aria-modal');\n    this._element.removeAttribute('role');\n    this._isTransitioning = false;\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n      this._resetAdjustments();\n      this._scrollBar.reset();\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY;\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n    this._element.classList.add(CLASS_NAME_STATIC);\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n    this._element.focus();\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const scrollbarWidth = this._scrollBar.getWidth();\n    const isBodyOverflowing = scrollbarWidth > 0;\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](relatedTarget);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  });\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$5;\n  }\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n  static get NAME() {\n    return NAME$6;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._backdrop.show();\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._focustrap.deactivate();\n    this._element.blur();\n    this._isShown = false;\n    this._element.classList.add(CLASS_NAME_HIDING);\n    this._backdrop.hide();\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n      this._element.removeAttribute('aria-modal');\n      this._element.removeAttribute('role');\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n    this._queueCallback(completeCallback, this._element, true);\n  }\n  dispose() {\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n      this.hide();\n    };\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n    });\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  });\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n    }\n    return true;\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  }\n\n  // Getters\n  static get Default() {\n    return Default$4;\n  }\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n  static get NAME() {\n    return NAME$5;\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n  changeContent(content) {\n    this._checkContent(content);\n    this._config.content = {\n      ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n    const template = templateWrapper.children[0];\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n    return template;\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n    this._checkContent(config.content);\n  }\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n    if (!templateElement) {\n      return;\n    }\n    content = this._resolvePossibleFunction(content);\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n      return;\n    }\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n    templateElement.textContent = content;\n  }\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this]);\n  }\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n    templateElement.textContent = element.textContent;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' + '<div class=\"tooltip-arrow\"></div>' + '<div class=\"tooltip-inner\"></div>' + '</div>',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n    super(element, config);\n\n    // Private\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null;\n\n    // Protected\n    this.tip = null;\n    this._setListeners();\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$3;\n  }\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n  static get NAME() {\n    return NAME$4;\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true;\n  }\n  disable() {\n    this._isEnabled = false;\n  }\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n    this._activeTrigger.click = !this._activeTrigger.click;\n    if (this._isShown()) {\n      this._leave();\n      return;\n    }\n    this._enter();\n  }\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n    this._disposePopper();\n    super.dispose();\n  }\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper();\n    const tip = this._getTipElement();\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n    const {\n      container\n    } = this._config;\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n      if (this._isHovered === false) {\n        this._leave();\n      }\n      this._isHovered = false;\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const tip = this._getTipElement();\n    tip.classList.remove(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n      this._element.removeAttribute('aria-describedby');\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n    return this.tip;\n  }\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml();\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null;\n    }\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n    return tip;\n  }\n  setContent(content) {\n    this._newContent = content;\n    if (this._isShown()) {\n      this._disposePopper();\n      this.show();\n    }\n  }\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n    return this._templateFactory;\n  }\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element]);\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element]);\n  }\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n          context._leave();\n        });\n      }\n    }\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n    if (!title) {\n      return;\n    }\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title');\n  }\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n    this._isHovered = true;\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n    this._isHovered = false;\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n    return config;\n  }\n  _getDelegateConfig() {\n    const config = {};\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value;\n      }\n    }\n    config.selector = false;\n    config.trigger = 'manual';\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config;\n  }\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n      this._popper = null;\n    }\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' + '<div class=\"popover-arrow\"></div>' + '<h3 class=\"popover-header\"></h3>' + '<div class=\"popover-body\"></div>' + '</div>',\n  trigger: 'click'\n};\nconst DefaultType$2 = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n  static get NAME() {\n    return NAME$3;\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default$1;\n  }\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n  static get NAME() {\n    return NAME$2;\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables();\n    this._maybeEnableSmoothScroll();\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n  dispose() {\n    this._observer.disconnect();\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body;\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n    return config;\n  }\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height;\n      }\n    });\n  }\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n      this._process(targetElement(entry));\n    };\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n        this._clearActiveClass(targetElement(entry));\n        continue;\n      }\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry);\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return;\n        }\n        continue;\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor);\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n    this._clearActiveClass(this._config.target);\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n    this._activateParents(target);\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE$1);\n      }\n    }\n  }\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE$1);\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE$1}`, parent);\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE$1);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API$1, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$1 = 'tab';\nconst DATA_KEY$1 = 'bs.tab';\nconst EVENT_KEY$1 = `.${DATA_KEY$1}`;\nconst EVENT_HIDE$1 = `hide${EVENT_KEY$1}`;\nconst EVENT_HIDDEN$1 = `hidden${EVENT_KEY$1}`;\nconst EVENT_SHOW$1 = `show${EVENT_KEY$1}`;\nconst EVENT_SHOWN$1 = `shown${EVENT_KEY$1}`;\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY$1}`;\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY$1}`;\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY$1}`;\nconst ARROW_LEFT_KEY = 'ArrowLeft';\nconst ARROW_RIGHT_KEY = 'ArrowRight';\nconst ARROW_UP_KEY = 'ArrowUp';\nconst ARROW_DOWN_KEY = 'ArrowDown';\nconst HOME_KEY = 'Home';\nconst END_KEY = 'End';\nconst CLASS_NAME_ACTIVE = 'active';\nconst CLASS_NAME_FADE$1 = 'fade';\nconst CLASS_NAME_SHOW$1 = 'show';\nconst CLASS_DROPDOWN = 'dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle';\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu';\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = `:not(${SELECTOR_DROPDOWN_TOGGLE})`;\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]';\nconst SELECTOR_OUTER = '.nav-item, .list-group-item';\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]'; // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`;\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element);\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL);\n    if (!this._parent) {\n      return;\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren());\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event));\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME$1;\n  }\n\n  // Public\n  show() {\n    // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element;\n    if (this._elemIsActive(innerElem)) {\n      return;\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem();\n    const hideEvent = active ? EventHandler.trigger(active, EVENT_HIDE$1, {\n      relatedTarget: innerElem\n    }) : null;\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW$1, {\n      relatedTarget: active\n    });\n    if (showEvent.defaultPrevented || hideEvent && hideEvent.defaultPrevented) {\n      return;\n    }\n    this._deactivate(active, innerElem);\n    this._activate(innerElem, active);\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.add(CLASS_NAME_ACTIVE);\n    this._activate(SelectorEngine.getElementFromSelector(element)); // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.removeAttribute('tabindex');\n      element.setAttribute('aria-selected', true);\n      this._toggleDropDown(element, true);\n      EventHandler.trigger(element, EVENT_SHOWN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.remove(CLASS_NAME_ACTIVE);\n    element.blur();\n    this._deactivate(SelectorEngine.getElementFromSelector(element)); // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.setAttribute('aria-selected', false);\n      element.setAttribute('tabindex', '-1');\n      this._toggleDropDown(element, false);\n      EventHandler.trigger(element, EVENT_HIDDEN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _keydown(event) {\n    if (![ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key)) {\n      return;\n    }\n    event.stopPropagation(); // stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault();\n    const children = this._getChildren().filter(element => !isDisabled(element));\n    let nextActiveElement;\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1];\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key);\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true);\n    }\n    if (nextActiveElement) {\n      nextActiveElement.focus({\n        preventScroll: true\n      });\n      Tab.getOrCreateInstance(nextActiveElement).show();\n    }\n  }\n  _getChildren() {\n    // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent);\n  }\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null;\n  }\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist');\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child);\n    }\n  }\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child);\n    const isActive = this._elemIsActive(child);\n    const outerElem = this._getOuterElement(child);\n    child.setAttribute('aria-selected', isActive);\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation');\n    }\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1');\n    }\n    this._setAttributeIfNotExists(child, 'role', 'tab');\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child);\n  }\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child);\n    if (!target) {\n      return;\n    }\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel');\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`);\n    }\n  }\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element);\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return;\n    }\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem);\n      if (element) {\n        element.classList.toggle(className, open);\n      }\n    };\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE);\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW$1);\n    outerElem.setAttribute('aria-expanded', open);\n  }\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value);\n    }\n  }\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE);\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem);\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  Tab.getOrCreateInstance(this).show();\n});\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME = 'toast';\nconst DATA_KEY = 'bs.toast';\nconst EVENT_KEY = `.${DATA_KEY}`;\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`;\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`;\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`;\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`;\nconst EVENT_HIDE = `hide${EVENT_KEY}`;\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`;\nconst EVENT_SHOW = `show${EVENT_KEY}`;\nconst EVENT_SHOWN = `shown${EVENT_KEY}`;\nconst CLASS_NAME_FADE = 'fade';\nconst CLASS_NAME_HIDE = 'hide'; // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show';\nconst CLASS_NAME_SHOWING = 'showing';\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n};\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n};\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._timeout = null;\n    this._hasMouseInteraction = false;\n    this._hasKeyboardInteraction = false;\n    this._setListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default;\n  }\n  static get DefaultType() {\n    return DefaultType;\n  }\n  static get NAME() {\n    return NAME;\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._clearTimeout();\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE);\n    }\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING);\n      EventHandler.trigger(this._element, EVENT_SHOWN);\n      this._maybeScheduleHide();\n    };\n    this._element.classList.remove(CLASS_NAME_HIDE); // @deprecated\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  hide() {\n    if (!this.isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE); // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW);\n      EventHandler.trigger(this._element, EVENT_HIDDEN);\n    };\n    this._element.classList.add(CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  dispose() {\n    this._clearTimeout();\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW);\n    }\n    super.dispose();\n  }\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW);\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return;\n    }\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return;\n    }\n    this._timeout = setTimeout(() => {\n      this.hide();\n    }, this._config.delay);\n  }\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout':\n        {\n          this._hasMouseInteraction = isInteracting;\n          break;\n        }\n      case 'focusin':\n      case 'focusout':\n        {\n          this._hasKeyboardInteraction = isInteracting;\n          break;\n        }\n    }\n    if (isInteracting) {\n      this._clearTimeout();\n      return;\n    }\n    const nextElement = event.relatedTarget;\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return;\n    }\n    this._maybeScheduleHide();\n  }\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false));\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false));\n  }\n  _clearTimeout() {\n    clearTimeout(this._timeout);\n    this._timeout = null;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config](this);\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast);\n\nexport { Alert, Button, Carousel, Collapse, Dropdown, Modal, Offcanvas, Popover, ScrollSpy, Tab, Toast, Tooltip };\n//# sourceMappingURL=bootstrap.esm.js.map\n","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","// Import and setup functions to control Bootstrap's behavior.\nimport \"@popperjs/core\";\nimport { Tooltip } from \"bootstrap\";\nimport { documentReady } from \"./mixin\";\n\nimport \"../styles/bootstrap.scss\";\n\n/*******************************************************************************\n * Trigger tooltips\n */\n\n/**\n * Add tooltip to each element with the \"tooltip\" data-bs-toogle class\n */\nfunction TriggerTooltip() {\n  var tooltipTriggerList = [].slice.call(\n    document.querySelectorAll('[data-bs-toggle=\"tooltip\"]')\n  );\n  tooltipTriggerList.map(function (tooltipTriggerEl) {\n    return new Tooltip(tooltipTriggerEl, { delay: { show: 500, hide: 100 } });\n  });\n}\n\n/*******************************************************************************\n * back to top button\n */\nfunction backToTop() {\n  var btn = document.getElementById(\"pst-back-to-top\");\n  btn.addEventListener(\"click\", function () {\n    document.body.scrollTop = 0;\n    document.documentElement.scrollTop = 0;\n  });\n}\n\nfunction showBackToTop() {\n  var btn = document.getElementById(\"pst-back-to-top\");\n  var header = document\n    .getElementsByClassName(\"bd-header\")[0]\n    .getBoundingClientRect();\n  window.addEventListener(\"scroll\", function () {\n    if (this.oldScroll > this.scrollY && this.scrollY > header.bottom) {\n      btn.style.display = \"block\";\n    } else {\n      btn.style.display = \"none\";\n    }\n    this.oldScroll = this.scrollY;\n  });\n}\n\n/*******************************************************************************\n * Call functions after document loading.\n 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Symbol(\"webpack queues\") : \"__webpack_queues__\";\nvar webpackExports = typeof Symbol === \"function\" ? Symbol(\"webpack exports\") : \"__webpack_exports__\";\nvar webpackError = typeof Symbol === \"function\" ? Symbol(\"webpack error\") : \"__webpack_error__\";\nvar resolveQueue = (queue) => {\n\tif(queue && queue.d < 1) {\n\t\tqueue.d = 1;\n\t\tqueue.forEach((fn) => (fn.r--));\n\t\tqueue.forEach((fn) => (fn.r-- ? fn.r++ : fn()));\n\t}\n}\nvar wrapDeps = (deps) => (deps.map((dep) => {\n\tif(dep !== null && typeof dep === \"object\") {\n\t\tif(dep[webpackQueues]) return dep;\n\t\tif(dep.then) {\n\t\t\tvar queue = [];\n\t\t\tqueue.d = 0;\n\t\t\tdep.then((r) => {\n\t\t\t\tobj[webpackExports] = r;\n\t\t\t\tresolveQueue(queue);\n\t\t\t}, (e) => {\n\t\t\t\tobj[webpackError] = e;\n\t\t\t\tresolveQueue(queue);\n\t\t\t});\n\t\t\tvar obj = {};\n\t\t\tobj[webpackQueues] = (fn) => (fn(queue));\n\t\t\treturn obj;\n\t\t}\n\t}\n\tvar ret = {};\n\tret[webpackQueues] = x => {};\n\tret[webpackExports] = dep;\n\treturn ret;\n}));\n__webpack_require__.a = (module, body, hasAwait) => {\n\tvar queue;\n\thasAwait && ((queue = []).d = -1);\n\tvar depQueues = new Set();\n\tvar exports = module.exports;\n\tvar currentDeps;\n\tvar outerResolve;\n\tvar reject;\n\tvar promise = new Promise((resolve, rej) => {\n\t\treject = rej;\n\t\touterResolve = resolve;\n\t});\n\tpromise[webpackExports] = exports;\n\tpromise[webpackQueues] = (fn) => (queue && fn(queue), depQueues.forEach(fn), promise[\"catch\"](x => {}));\n\tmodule.exports = promise;\n\tbody((deps) => {\n\t\tcurrentDeps = wrapDeps(deps);\n\t\tvar fn;\n\t\tvar getResult = () => (currentDeps.map((d) => {\n\t\t\tif(d[webpackError]) throw d[webpackError];\n\t\t\treturn d[webpackExports];\n\t\t}))\n\t\tvar promise = new Promise((resolve) => {\n\t\t\tfn = () => (resolve(getResult));\n\t\t\tfn.r = 0;\n\t\t\tvar fnQueue = (q) => (q !== queue && !depQueues.has(q) && (depQueues.add(q), q && !q.d && (fn.r++, q.push(fn))));\n\t\t\tcurrentDeps.map((dep) => (dep[webpackQueues](fnQueue)));\n\t\t});\n\t\treturn fn.r ? promise : getResult();\n\t}, (err) => ((err ? reject(promise[webpackError] = err) : outerResolve(exports)), resolveQueue(queue)));\n\tqueue && queue.d < 0 && (queue.d = 0);\n};","/**\n * Compare [semver](https://semver.org/) version strings to find greater, equal or lesser.\n * This library supports the full semver specification, including comparing versions with different number of digits like `1.0.0`, `1.0`, `1`, and pre-release versions like `1.0.0-alpha`.\n * @param v1 - First version to compare\n * @param v2 - Second version to compare\n * @returns Numeric value compatible with the [Array.sort(fn) interface](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/sort#Parameters).\n */\nexport const compareVersions = (v1, v2) => {\n    // validate input and split into segments\n    const n1 = validateAndParse(v1);\n    const n2 = validateAndParse(v2);\n    // pop off the patch\n    const p1 = n1.pop();\n    const p2 = n2.pop();\n    // validate numbers\n    const r = compareSegments(n1, n2);\n    if (r !== 0)\n        return r;\n    // validate pre-release\n    if (p1 && p2) {\n        return compareSegments(p1.split('.'), p2.split('.'));\n    }\n    else if (p1 || p2) {\n        return p1 ? -1 : 1;\n    }\n    return 0;\n};\n/**\n * Validate [semver](https://semver.org/) version strings.\n *\n * @param version Version number to validate\n * @returns `true` if the version number is a valid semver version number, `false` otherwise.\n *\n * @example\n * ```\n * validate('1.0.0-rc.1'); // return true\n * validate('1.0-rc.1'); // return false\n * validate('foo'); // return false\n * ```\n */\nexport const validate = (version) => typeof version === 'string' && /^[v\\d]/.test(version) && semver.test(version);\n/**\n * Compare [semver](https://semver.org/) version strings using the specified operator.\n *\n * @param v1 First version to compare\n * @param v2 Second version to compare\n * @param operator Allowed arithmetic operator to use\n * @returns `true` if the comparison between the firstVersion and the secondVersion satisfies the operator, `false` otherwise.\n *\n * @example\n * ```\n * compare('10.1.8', '10.0.4', '>'); // return true\n * compare('10.0.1', '10.0.1', '='); // return true\n * compare('10.1.1', '10.2.2', '<'); // return true\n * compare('10.1.1', '10.2.2', '<='); // return true\n * compare('10.1.1', '10.2.2', '>='); // return false\n * ```\n */\nexport const compare = (v1, v2, operator) => {\n    // validate input operator\n    assertValidOperator(operator);\n    // since result of compareVersions can only be -1 or 0 or 1\n    // a simple map can be used to replace switch\n    const res = compareVersions(v1, v2);\n    return operatorResMap[operator].includes(res);\n};\n/**\n * Match [npm semver](https://docs.npmjs.com/cli/v6/using-npm/semver) version range.\n *\n * @param version Version number to match\n * @param range Range pattern for version\n * @returns `true` if the version number is within the range, `false` otherwise.\n *\n * @example\n * ```\n * satisfies('1.1.0', '^1.0.0'); // return true\n * satisfies('1.1.0', '~1.0.0'); // return false\n * ```\n */\nexport const satisfies = (version, range) => {\n    // if no range operator then \"=\"\n    const m = range.match(/^([<>=~^]+)/);\n    const op = m ? m[1] : '=';\n    // if gt/lt/eq then operator compare\n    if (op !== '^' && op !== '~')\n        return compare(version, range, op);\n    // else range of either \"~\" or \"^\" is assumed\n    const [v1, v2, v3, , vp] = validateAndParse(version);\n    const [r1, r2, r3, , rp] = validateAndParse(range);\n    const v = [v1, v2, v3];\n    const r = [r1, r2 !== null && r2 !== void 0 ? r2 : 'x', r3 !== null && r3 !== void 0 ? r3 : 'x'];\n    // validate pre-release\n    if (rp) {\n        if (!vp)\n            return false;\n        if (compareSegments(v, r) !== 0)\n            return false;\n        if (compareSegments(vp.split('.'), rp.split('.')) === -1)\n            return false;\n    }\n    // first non-zero number\n    const nonZero = r.findIndex((v) => v !== '0') + 1;\n    // pointer to where segments can be >=\n    const i = op === '~' ? 2 : nonZero > 1 ? nonZero : 1;\n    // before pointer must be equal\n    if (compareSegments(v.slice(0, i), r.slice(0, i)) !== 0)\n        return false;\n    // after pointer must be >=\n    if (compareSegments(v.slice(i), r.slice(i)) === -1)\n        return false;\n    return true;\n};\nconst semver = /^[v^~<>=]*?(\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+))?(?:-([\\da-z\\-]+(?:\\.[\\da-z\\-]+)*))?(?:\\+[\\da-z\\-]+(?:\\.[\\da-z\\-]+)*)?)?)?$/i;\nconst validateAndParse = (version) => {\n    if (typeof version !== 'string') {\n        throw new TypeError('Invalid argument expected string');\n    }\n    const match = version.match(semver);\n    if (!match) {\n        throw new Error(`Invalid argument not valid semver ('${version}' received)`);\n    }\n    match.shift();\n    return match;\n};\nconst isWildcard = (s) => s === '*' || s === 'x' || s === 'X';\nconst tryParse = (v) => {\n    const n = parseInt(v, 10);\n    return isNaN(n) ? v : n;\n};\nconst forceType = (a, b) => typeof a !== typeof b ? [String(a), String(b)] : [a, b];\nconst compareStrings = (a, b) => {\n    if (isWildcard(a) || isWildcard(b))\n        return 0;\n    const [ap, bp] = forceType(tryParse(a), tryParse(b));\n    if (ap > bp)\n        return 1;\n    if (ap < bp)\n        return -1;\n    return 0;\n};\nconst compareSegments = (a, b) => {\n    for (let i = 0; i < Math.max(a.length, b.length); i++) {\n        const r = compareStrings(a[i] || '0', b[i] || '0');\n        if (r !== 0)\n            return r;\n    }\n    return 0;\n};\nconst operatorResMap = {\n    '>': [1],\n    '>=': [0, 1],\n    '=': [0],\n    '<=': [-1, 0],\n    '<': [-1],\n};\nconst allowedOperators = Object.keys(operatorResMap);\nconst assertValidOperator = (op) => {\n    if (typeof op !== 'string') {\n        throw new TypeError(`Invalid operator type, expected string but got ${typeof op}`);\n    }\n    if (allowedOperators.indexOf(op) === -1) {\n        throw new Error(`Invalid operator, expected one of ${allowedOperators.join('|')}`);\n    }\n};\n//# sourceMappingURL=index.js.map","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","// Define the custom behavior of the page\nimport { documentReady } from \"./mixin\";\nimport { compare, validate } from \"compare-versions\";\n\nimport \"../styles/pydata-sphinx-theme.scss\";\n\n/*******************************************************************************\n * Theme interaction\n */\n\nvar prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\");\n\n/**\n * set the the body theme to the one specified by the user browser\n *\n * @param {event} e\n */\nfunction autoTheme(e) {\n  document.documentElement.dataset.theme = prefersDark.matches\n    ? \"dark\"\n    : \"light\";\n}\n\n/**\n * Set the theme using the specified mode.\n * It can be one of [\"auto\", \"dark\", \"light\"]\n *\n * @param {str} mode\n */\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  // get the theme\n  var colorScheme = prefersDark.matches ? \"dark\" : \"light\";\n  document.documentElement.dataset.mode = mode;\n  var theme = mode == \"auto\" ? colorScheme : mode;\n  document.documentElement.dataset.theme = theme;\n  // TODO: remove this line after Bootstrap upgrade\n  // v5.3 has a colors mode: https://getbootstrap.com/docs/5.3/customize/color-modes/\n  document.querySelectorAll(\".dropdown-menu\").forEach((el) => {\n    if (theme === \"dark\") {\n      el.classList.add(\"dropdown-menu-dark\");\n    } else {\n      el.classList.remove(\"dropdown-menu-dark\");\n    }\n  });\n\n  // save mode and theme\n  localStorage.setItem(\"mode\", mode);\n  localStorage.setItem(\"theme\", theme);\n  console.log(`[PST]: Changed to ${mode} mode using the ${theme} theme.`);\n\n  // add a listener if set on auto\n  prefersDark.onchange = mode == \"auto\" ? autoTheme : \"\";\n}\n\n/**\n * Change the theme option order so that clicking on the btn is always a change\n * from \"auto\"\n */\nfunction cycleMode() {\n  const defaultMode = document.documentElement.dataset.defaultMode || \"auto\";\n  const currentMode = localStorage.getItem(\"mode\") || defaultMode;\n\n  var loopArray = (arr, current) => {\n    var nextPosition = arr.indexOf(current) + 1;\n    if (nextPosition === arr.length) {\n      nextPosition = 0;\n    }\n    return arr[nextPosition];\n  };\n\n  // make sure the next theme after auto is always a change\n  var modeList = prefersDark.matches\n    ? [\"auto\", \"light\", \"dark\"]\n    : [\"auto\", \"dark\", \"light\"];\n  var newMode = loopArray(modeList, currentMode);\n  setTheme(newMode);\n}\n\n/**\n * add the theme listener on the btns of the navbar\n */\nfunction addModeListener() {\n  // the theme was set a first time using the initial mini-script\n  // running setMode will ensure the use of the dark mode if auto is selected\n  setTheme(document.documentElement.dataset.mode);\n\n  // Attach event handlers for toggling themes colors\n  document.querySelectorAll(\".theme-switch-button\").forEach((el) => {\n    el.addEventListener(\"click\", cycleMode);\n  });\n}\n\n/*******************************************************************************\n * TOC interactivity\n */\n\n/**\n * TOC sidebar - add \"active\" class to parent list\n *\n * Bootstrap's scrollspy adds the active class to the <a> link,\n * but for the automatic collapsing we need this on the parent list item.\n *\n * The event is triggered on \"window\" (and not the nav item as documented),\n * see https://github.com/twbs/bootstrap/issues/20086\n */\nfunction addTOCInteractivity() {\n  window.addEventListener(\"activate.bs.scrollspy\", function () {\n    const navLinks = document.querySelectorAll(\".bd-toc-nav a\");\n\n    navLinks.forEach((navLink) => {\n      navLink.parentElement.classList.remove(\"active\");\n    });\n\n    const activeNavLinks = document.querySelectorAll(\".bd-toc-nav a.active\");\n    activeNavLinks.forEach((navLink) => {\n      navLink.parentElement.classList.add(\"active\");\n    });\n  });\n}\n\n/*******************************************************************************\n * Scroll\n */\n\n/**\n * Navigation sidebar scrolling to active page\n */\nfunction scrollToActive() {\n  // If the docs nav doesn't exist, do nothing (e.g., on search page)\n  if (!document.querySelector(\".bd-docs-nav\")) {\n    return;\n  }\n\n  var sidebar = document.querySelector(\"div.bd-sidebar\");\n\n  // Remember the sidebar scroll position between page loads\n  // Inspired on source of revealjs.com\n  let storedScrollTop = parseInt(\n    sessionStorage.getItem(\"sidebar-scroll-top\"),\n    10\n  );\n\n  if (!isNaN(storedScrollTop)) {\n    // If we've got a saved scroll position, just use that\n    sidebar.scrollTop = storedScrollTop;\n    console.log(\"[PST]: Scrolled sidebar using stored browser position...\");\n  } else {\n    // Otherwise, calculate a position to scroll to based on the lowest `active` link\n    var sidebarNav = document.querySelector(\".bd-docs-nav\");\n    var active_pages = sidebarNav.querySelectorAll(\".active\");\n    if (active_pages.length > 0) {\n      // Use the last active page as the offset since it's the page we're on\n      var latest_active = active_pages[active_pages.length - 1];\n      var offset =\n        latest_active.getBoundingClientRect().y -\n        sidebar.getBoundingClientRect().y;\n      // Only scroll the navbar if the active link is lower than 50% of the page\n      if (latest_active.getBoundingClientRect().y > window.innerHeight * 0.5) {\n        let buffer = 0.25; // Buffer so we have some space above the scrolled item\n        sidebar.scrollTop = offset - sidebar.clientHeight * buffer;\n        console.log(\"[PST]: Scrolled sidebar using last active link...\");\n      }\n    }\n  }\n\n  // Store the sidebar scroll position\n  window.addEventListener(\"beforeunload\", () => {\n    sessionStorage.setItem(\"sidebar-scroll-top\", sidebar.scrollTop);\n  });\n}\n\n/*******************************************************************************\n * Search\n */\n\n/**\n * Find any search forms on the page and return their input element\n */\nvar findSearchInput = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  if (!forms.length) {\n    // no search form found\n    return;\n  } else {\n    var form;\n    if (forms.length == 1) {\n      // there is exactly one search form (persistent or hidden)\n      form = forms[0];\n    } else {\n      // must be at least one persistent form, use the first persistent one\n      form = document.querySelector(\n        \"div:not(.search-button__search-container) > form.bd-search\"\n      );\n    }\n    return form.querySelector(\"input\");\n  }\n};\n\n/**\n * Activate the search field on the page.\n * - If there is a search field already visible it will be activated.\n * - If not, then a search field will pop up.\n */\nvar toggleSearchField = () => {\n  // Find the search input to highlight\n  let input = findSearchInput();\n\n  // if the input field is the hidden one (the one associated with the\n  // search button) then toggle the button state (to show/hide the field)\n  let searchPopupWrapper = document.querySelector(\".search-button__wrapper\");\n  let hiddenInput = searchPopupWrapper.querySelector(\"input\");\n  if (input === hiddenInput) {\n    searchPopupWrapper.classList.toggle(\"show\");\n  }\n  // when toggling off the search field, remove its focus\n  if (document.activeElement === input) {\n    input.blur();\n  } else {\n    input.focus();\n    input.select();\n    input.scrollIntoView({ block: \"center\" });\n  }\n};\n\n/**\n * Add an event listener for toggleSearchField() for Ctrl/Cmd + K\n */\nvar addEventListenerForSearchKeyboard = () => {\n  window.addEventListener(\n    \"keydown\",\n    (event) => {\n      let input = findSearchInput();\n      // toggle on Ctrl+k or ⌘+k\n      if (\n        // Ignore if shift or alt are pressed\n        !event.shiftKey &&\n        !event.altKey &&\n        // On Mac use ⌘, all other OS use Ctrl\n        (useCommandKey\n          ? event.metaKey && !event.ctrlKey\n          : !event.metaKey && event.ctrlKey) &&\n        // Case-insensitive so the shortcut still works with caps lock\n        /k/i.test(event.key)\n      ) {\n        event.preventDefault();\n        toggleSearchField();\n      }\n      // also allow Escape key to hide (but not show) the dynamic search field\n      else if (document.activeElement === input && /Escape/i.test(event.key)) {\n        toggleSearchField();\n      }\n    },\n    true\n  );\n};\n\n/**\n * If the user is on a Mac, use command (⌘) instead of control (ctrl) key\n *\n * Note: `navigator.platform` is deprecated; however MDN still recommends using\n * it for the one specific use case of detecting whether a keyboard shortcut\n * should use control or command:\n * https://developer.mozilla.org/en-US/docs/Web/API/Navigator/platform#examples\n */\nvar useCommandKey =\n  navigator.platform.indexOf(\"Mac\") === 0 || navigator.platform === \"iPhone\";\n\n/**\n * Change the search hint to `meta key` if we are a Mac\n */\n\nvar changeSearchShortcutKey = () => {\n  let shortcuts = document.querySelectorAll(\".search-button__kbd-shortcut\");\n  if (useCommandKey) {\n    shortcuts.forEach(\n      (f) => (f.querySelector(\"kbd.kbd-shortcut__modifier\").innerText = \"⌘\")\n    );\n  }\n};\n\n/**\n * Activate callbacks for search button popup\n */\nvar setupSearchButtons = () => {\n  changeSearchShortcutKey();\n  addEventListenerForSearchKeyboard();\n\n  // Add the search button trigger event callback\n  document.querySelectorAll(\".search-button__button\").forEach((btn) => {\n    btn.onclick = toggleSearchField;\n  });\n\n  // Add the search button overlay event callback\n  let overlay = document.querySelector(\".search-button__overlay\");\n  if (overlay) {\n    overlay.onclick = toggleSearchField;\n  }\n};\n\n/*******************************************************************************\n * Version Switcher\n * Note that this depends on two variables existing that are defined in\n * and `html-page-context` hook:\n *\n * - DOCUMENTATION_OPTIONS.pagename\n * - DOCUMENTATION_OPTIONS.theme_switcher_url\n */\n\n/**\n * Check if corresponding page path exists in other version of docs\n * and, if so, go there instead of the homepage of the other docs version\n *\n * @param {event} event the event that trigger the check\n */\nasync function checkPageExistsAndRedirect(event) {\n  // ensure we don't follow the initial link\n  event.preventDefault();\n  let currentFilePath = `${DOCUMENTATION_OPTIONS.pagename}.html`;\n  let tryUrl = event.currentTarget.getAttribute(\"href\");\n  let otherDocsHomepage = tryUrl.replace(currentFilePath, \"\");\n  try {\n    let head = await fetch(tryUrl, { method: \"HEAD\" });\n    if (head.ok) {\n      location.href = tryUrl; // the page exists, go there\n    } else {\n      location.href = otherDocsHomepage;\n    }\n  } catch (err) {\n    // something went wrong, probably CORS restriction, fallback to other docs homepage\n    location.href = otherDocsHomepage;\n  }\n}\n\n/**\n * Load and parse the version switcher JSON file from an absolute or relative URL.\n *\n * @param {string} url The URL to load version switcher entries from.\n */\nasync function fetchVersionSwitcherJSON(url) {\n  // first check if it's a valid URL\n  try {\n    var result = new URL(url);\n  } catch (err) {\n    if (err instanceof TypeError) {\n      // assume we got a relative path, and fix accordingly. But first, we need to\n      // use `fetch()` to follow redirects so we get the correct final base URL\n      const origin = await fetch(window.location.origin, { method: \"HEAD\" });\n      result = new URL(url, origin.url);\n    } else {\n      // something unexpected happened\n      throw err;\n    }\n  }\n  // load and return the JSON\n  const response = await fetch(result);\n  const data = await response.json();\n  return data;\n}\n\n// Populate the version switcher from the JSON data\nfunction populateVersionSwitcher(data, versionSwitcherBtns) {\n  const currentFilePath = `${DOCUMENTATION_OPTIONS.pagename}.html`;\n  versionSwitcherBtns.forEach((btn) => {\n    // Set empty strings by default so that these attributes exist and can be used in CSS selectors\n    btn.dataset[\"activeVersionName\"] = \"\";\n    btn.dataset[\"activeVersion\"] = \"\";\n  });\n  // in case there are multiple entries with the same version string, this helps us\n  // decide which entry's `name` to put on the button itself. Without this, it would\n  // always be the *last* version-matching entry; now it will be either the\n  // version-matching entry that is also marked as `\"preferred\": true`, or if that\n  // doesn't exist: the *first* version-matching entry.\n  data = data.map((entry) => {\n    // does this entry match the version that we're currently building/viewing?\n    entry.match =\n      entry.version == DOCUMENTATION_OPTIONS.theme_switcher_version_match;\n    entry.preferred = entry.preferred || false;\n    // if no custom name specified (e.g., \"latest\"), use version string\n    if (!(\"name\" in entry)) {\n      entry.name = entry.version;\n    }\n    return entry;\n  });\n  const hasMatchingPreferredEntry = data\n    .map((entry) => entry.preferred && entry.match)\n    .some(Boolean);\n  var foundMatch = false;\n  // create links to the corresponding page in the other docs versions\n  data.forEach((entry) => {\n    // create the node\n    const anchor = document.createElement(\"a\");\n    anchor.setAttribute(\n      \"class\",\n      \"dropdown-item list-group-item list-group-item-action py-1\"\n    );\n    anchor.setAttribute(\"href\", `${entry.url}${currentFilePath}`);\n    anchor.setAttribute(\"role\", \"option\");\n    const span = document.createElement(\"span\");\n    span.textContent = `${entry.name}`;\n    anchor.appendChild(span);\n    // Add dataset values for the version and name in case people want\n    // to apply CSS styling based on this information.\n    anchor.dataset[\"versionName\"] = entry.name;\n    anchor.dataset[\"version\"] = entry.version;\n    // replace dropdown button text with the preferred display name of the\n    // currently-viewed version, rather than using sphinx's {{ version }} variable.\n    // also highlight the dropdown entry for the currently-viewed version's entry\n    let matchesAndIsPreferred = hasMatchingPreferredEntry && entry.preferred;\n    let matchesAndIsFirst =\n      !hasMatchingPreferredEntry && !foundMatch && entry.match;\n    if (matchesAndIsPreferred || matchesAndIsFirst) {\n      anchor.classList.add(\"active\");\n      versionSwitcherBtns.forEach((btn) => {\n        btn.innerText = entry.name;\n        btn.dataset[\"activeVersionName\"] = entry.name;\n        btn.dataset[\"activeVersion\"] = entry.version;\n      });\n      foundMatch = true;\n    }\n    // There may be multiple version-switcher elements, e.g. one\n    // in a slide-over panel displayed on smaller screens.\n    document.querySelectorAll(\".version-switcher__menu\").forEach((menu) => {\n      // we need to clone the node for each menu, but onclick attributes are not\n      // preserved by `.cloneNode()` so we add onclick here after cloning.\n      let node = anchor.cloneNode(true);\n      node.onclick = checkPageExistsAndRedirect;\n      // on click, AJAX calls will check if the linked page exists before\n      // trying to redirect, and if not, will redirect to the homepage\n      // for that version of the docs.\n      menu.append(node);\n    });\n  });\n}\n\n/*******************************************************************************\n * Warning banner when viewing non-stable version of the docs.\n */\n\n/**\n * Show a warning banner when viewing a non-stable version of the docs.\n *\n * adapted 2023-06 from https://mne.tools/versionwarning.js, which was\n * originally adapted 2020-05 from https://scikit-learn.org/versionwarning.js\n *\n * @param {Array} data The version data used to populate the switcher menu.\n */\nfunction showVersionWarningBanner(data) {\n  var version = DOCUMENTATION_OPTIONS.VERSION;\n  // figure out what latest stable version is\n  var preferredEntries = data.filter((entry) => entry.preferred);\n  if (preferredEntries.length !== 1) {\n    const howMany = preferredEntries.length == 0 ? \"No\" : \"Multiple\";\n    console.log(\n      `[PST] ${howMany} versions marked \"preferred\" found in versions JSON, ignoring.`\n    );\n    return;\n  }\n  const preferredVersion = 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This is primarily two things:\n *\n * 1. Hide the Table of Contents any time sidebar content is on the screen.\n *\n * This will be triggered any time a sidebar item enters or exits the screen.\n * It adds/removes items from an array if they have entered the screen, and\n * removes them when they exit the screen. It hides the TOC if anything is\n * on-screen.\n *\n * ref: https://developer.mozilla.org/en-US/docs/Web/API/Intersection_Observer_API\n *\n * 2. Add a `scrolled` class to <body> to trigger CSS changes.\n */\nvar initTocHide = () => {\n  var onScreenItems = [];\n  let hideTocCallback = (entries, observer) => {\n    // Check whether any sidebar item is displayed\n    entries.forEach((entry) => {\n      if (entry.isIntersecting) {\n        // If an element just came on screen, add it our list\n        onScreenItems.push(entry.target);\n      } else {\n        // Otherwise, if it's in our list then remove it\n        for (let ii = 0; ii < onScreenItems.length; ii++) {\n          if (onScreenItems[ii] === entry.target) {\n            onScreenItems.splice(ii, 1);\n            break;\n          }\n        }\n      }\n    });\n\n    // Hide the TOC if any margin content is displayed on the screen\n    if (onScreenItems.length > 0) {\n      document.querySelector(\"div.bd-sidebar-secondary\").classList.add(\"hide\");\n    } else {\n      document\n        .querySelector(\"div.bd-sidebar-secondary\")\n        .classList.remove(\"hide\");\n    }\n  };\n  let manageScrolledClassOnBody = (entries, observer) => {\n    // The pixel is at the top, so if we're < 0 that it means we've scrolled\n    if (entries[0].boundingClientRect.y < 0) {\n      document.body.classList.add(\"scrolled\");\n    } else {\n      document.body.classList.remove(\"scrolled\");\n    }\n  };\n\n  // Set up the intersection observer to watch all margin content\n  let options = {\n    // Trigger callback when the top of a margin item is 1/3 up the screen\n    rootMargin: \"0px 0px -33% 0px\",\n  };\n  let tocObserver = new IntersectionObserver(hideTocCallback, options);\n  // TODO: deprecate popout after v0.5.0\n  const selectorClasses = [\n    \"marginnote\",\n    \"sidenote\",\n    \"margin\",\n    \"margin-caption\",\n    \"full-width\",\n    \"sidebar\",\n    \"popout\",\n  ];\n  let marginSelector = [];\n  selectorClasses.forEach((ii) => {\n    // Use three permutations of each class name because `tag_` and `_` used to be supported\n    marginSelector.push(\n      ...[\n        `.${ii}`,\n        `.tag_${ii}`,\n        `.${ii.replace(\"-\", \"_\")}`,\n        `.tag_${ii.replace(\"-\", \"_\")}`,\n      ],\n    );\n  });\n  document.querySelectorAll(marginSelector.join(\", \")).forEach((ii) => {\n    tocObserver.observe(ii);\n  });\n\n  // Set up the observer to check if we've scrolled from top of page\n  let scrollObserver = new IntersectionObserver(manageScrolledClassOnBody);\n  scrollObserver.observe(document.querySelector(\".sbt-scroll-pixel-helper\"));\n};\n\n/**\n * Activate Thebe with a custom button click.\n */\nvar initThebeSBT = () => {\n  var title = document.querySelector(\"section h1\");\n  var sibling = title.nextElementSibling;\n  // If the next element after the title isn't a thebe button, add one now.\n  // That way it is initiatlized when thebe is first-clicked and isn't re-added after.\n  if (!sibling.classList.contains(\"thebe-launch-button\")) {\n    title.insertAdjacentHTML(\n      \"afterend\",\n      \"<button class='thebe-launch-button'></button>\",\n    );\n  }\n  // This function is provided by sphinx-thebe\n  initThebe();\n};\n\n/**\n * Add no print class to certain DOM elements\n */\n\nfunction addNoPrint() {\n  var noPrintSelector = [\n    \".bd-header-announcement\",\n    \".bd-header\",\n    \".bd-header-article\",\n    \".bd-sidebar-primary\",\n    \".bd-sidebar-secondary\",\n    \".bd-footer-article\",\n    \".bd-footer-content\",\n    \".bd-footer\",\n  ].join(\",\");\n  document.querySelectorAll(noPrintSelector).forEach((ii) => {\n    ii.classList.add(\"noprint\");\n  });\n}\n\n/**\n * Set up callback functions for UI click actions\n */\nwindow.initThebeSBT = initThebeSBT;\nwindow.toggleFullScreen = toggleFullScreen;\n\n/**\n * Set up functions to load when the DOM is ready\n */\nsbRunWhenDOMLoaded(initTocHide);\nsbRunWhenDOMLoaded(addNoPrint);\n"],"sourceRoot":""}
\ No newline at end of file
diff --git a/2023.2/_static/styles/bootstrap.css b/2023.2/_static/styles/bootstrap.css
new file mode 100644
index 0000000000..82b03c0cd1
--- /dev/null
+++ b/2023.2/_static/styles/bootstrap.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap  v5.3.2 (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ */:root,[data-bs-theme=light]{--bs-blue:#0d6efd;--bs-indigo:#6610f2;--bs-purple:#6f42c1;--bs-pink:#d63384;--bs-red:#dc3545;--bs-orange:#fd7e14;--bs-yellow:#ffc107;--bs-green:#198754;--bs-teal:#20c997;--bs-cyan:#0dcaf0;--bs-black:#000;--bs-white:#fff;--bs-gray:#6c757d;--bs-gray-dark:#343a40;--bs-gray-100:#f8f9fa;--bs-gray-200:#e9ecef;--bs-gray-300:#dee2e6;--bs-gray-400:#ced4da;--bs-gray-500:#adb5bd;--bs-gray-600:#6c757d;--bs-gray-700:#495057;--bs-gray-800:#343a40;--bs-gray-900:#212529;--bs-primary:#0d6efd;--bs-secondary:#6c757d;--bs-success:#198754;--bs-info:#0dcaf0;--bs-warning:#ffc107;--bs-danger:#dc3545;--bs-light:#f8f9fa;--bs-dark:#212529;--bs-primary-rgb:13,110,253;--bs-secondary-rgb:108,117,125;--bs-success-rgb:25,135,84;--bs-info-rgb:13,202,240;--bs-warning-rgb:255,193,7;--bs-danger-rgb:220,53,69;--bs-light-rgb:248,249,250;--bs-dark-rgb:33,37,41;--bs-primary-text-emphasis:#052c65;--bs-secondary-text-emphasis:#2b2f32;--bs-success-text-emphasis:#0a3622;--bs-info-text-emphasis:#055160;--bs-warning-text-emphasis:#664d03;--bs-danger-text-emphasis:#58151c;--bs-light-text-emphasis:#495057;--bs-dark-text-emphasis:#495057;--bs-primary-bg-subtle:#cfe2ff;--bs-secondary-bg-subtle:#e2e3e5;--bs-success-bg-subtle:#d1e7dd;--bs-info-bg-subtle:#cff4fc;--bs-warning-bg-subtle:#fff3cd;--bs-danger-bg-subtle:#f8d7da;--bs-light-bg-subtle:#fcfcfd;--bs-dark-bg-subtle:#ced4da;--bs-primary-border-subtle:#9ec5fe;--bs-secondary-border-subtle:#c4c8cb;--bs-success-border-subtle:#a3cfbb;--bs-info-border-subtle:#9eeaf9;--bs-warning-border-subtle:#ffe69c;--bs-danger-border-subtle:#f1aeb5;--bs-light-border-subtle:#e9ecef;--bs-dark-border-subtle:#adb5bd;--bs-white-rgb:255,255,255;--bs-black-rgb:0,0,0;--bs-font-sans-serif:system-ui,-apple-system,"Segoe 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.accordion-collapse{border-bottom-left-radius:var(--bs-accordion-border-radius);border-bottom-right-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-left:0;border-radius:0;border-right:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button:after{--bs-accordion-btn-icon:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3E%3Cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3E%3C/svg%3E");--bs-accordion-btn-active-icon:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3E%3Cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3E%3C/svg%3E")}.breadcrumb{--bs-breadcrumb-padding-x:0;--bs-breadcrumb-padding-y:0;--bs-breadcrumb-margin-bottom:1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color:var(--bs-secondary-color);--bs-breadcrumb-item-padding-x:0.5rem;--bs-breadcrumb-item-active-color:var(--bs-secondary-color);background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius);display:flex;flex-wrap:wrap;font-size:var(--bs-breadcrumb-font-size);list-style:none;margin-bottom:var(--bs-breadcrumb-margin-bottom);padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 0 0 0.25rem rgba(13,110,253,.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;list-style:none;padding-left:0}.page-link{background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);color:var(--bs-pagination-color);display:block;font-size:var(--bs-pagination-font-size);padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);position:relative;text-decoration:none;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion:reduce){.page-link{transition:none}}.page-link:hover{background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color);color:var(--bs-pagination-hover-color);z-index:2}.page-link:focus{background-color:var(--bs-pagination-focus-bg);box-shadow:var(--bs-pagination-focus-box-shadow);color:var(--bs-pagination-focus-color);outline:0;z-index:3}.active>.page-link,.page-link.active{background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color);color:var(--bs-pagination-active-color);z-index:3}.disabled>.page-link,.page-link.disabled{background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color);color:var(--bs-pagination-disabled-color);pointer-events:none}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width)*-1)}.page-item:first-child .page-link{border-bottom-left-radius:var(--bs-pagination-border-radius);border-top-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-bottom-right-radius:var(--bs-pagination-border-radius);border-top-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x:1.5rem;--bs-pagination-padding-y:0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius:var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x:0.5rem;--bs-pagination-padding-y:0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius:var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x:0.65em;--bs-badge-padding-y:0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight:700;--bs-badge-color:#fff;--bs-badge-border-radius:var(--bs-border-radius);border-radius:var(--bs-badge-border-radius);color:var(--bs-badge-color);display:inline-block;font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);text-align:center;vertical-align:baseline;white-space:nowrap}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg:transparent;--bs-alert-padding-x:1rem;--bs-alert-padding-y:1rem;--bs-alert-margin-bottom:1rem;--bs-alert-color:inherit;--bs-alert-border-color:transparent;--bs-alert-border:var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius:var(--bs-border-radius);--bs-alert-link-color:inherit;background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius);color:var(--bs-alert-color);margin-bottom:var(--bs-alert-margin-bottom);padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);position:relative}.alert-heading{color:inherit}.alert-link{color:var(--bs-alert-link-color);font-weight:700}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{padding:1.25rem 1rem;position:absolute;right:0;top:0;z-index:2}.alert-primary{--bs-alert-color:var(--bs-primary-text-emphasis);--bs-alert-bg:var(--bs-primary-bg-subtle);--bs-alert-border-color:var(--bs-primary-border-subtle);--bs-alert-link-color:var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color:var(--bs-secondary-text-emphasis);--bs-alert-bg:var(--bs-secondary-bg-subtle);--bs-alert-border-color:var(--bs-secondary-border-subtle);--bs-alert-link-color:var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color:var(--bs-success-text-emphasis);--bs-alert-bg:var(--bs-success-bg-subtle);--bs-alert-border-color:var(--bs-success-border-subtle);--bs-alert-link-color:var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color:var(--bs-info-text-emphasis);--bs-alert-bg:var(--bs-info-bg-subtle);--bs-alert-border-color:var(--bs-info-border-subtle);--bs-alert-link-color:var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color:var(--bs-warning-text-emphasis);--bs-alert-bg:var(--bs-warning-bg-subtle);--bs-alert-border-color:var(--bs-warning-border-subtle);--bs-alert-link-color:var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color:var(--bs-danger-text-emphasis);--bs-alert-bg:var(--bs-danger-bg-subtle);--bs-alert-border-color:var(--bs-danger-border-subtle);--bs-alert-link-color:var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color:var(--bs-light-text-emphasis);--bs-alert-bg:var(--bs-light-bg-subtle);--bs-alert-border-color:var(--bs-light-border-subtle);--bs-alert-link-color:var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color:var(--bs-dark-text-emphasis);--bs-alert-bg:var(--bs-dark-bg-subtle);--bs-alert-border-color:var(--bs-dark-border-subtle);--bs-alert-link-color:var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height:1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg:var(--bs-secondary-bg);--bs-progress-border-radius:var(--bs-border-radius);--bs-progress-box-shadow:var(--bs-box-shadow-inset);--bs-progress-bar-color:#fff;--bs-progress-bar-bg:#0d6efd;--bs-progress-bar-transition:width 0.6s ease;background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius);display:flex;font-size:var(--bs-progress-font-size);height:var(--bs-progress-height);overflow:hidden}.progress-bar{background-color:var(--bs-progress-bar-bg);color:var(--bs-progress-bar-color);display:flex;flex-direction:column;justify-content:center;overflow:hidden;text-align:center;transition:var(--bs-progress-bar-transition);white-space:nowrap}@media(prefers-reduced-motion:reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg,hsla(0,0%,100%,.15) 25%,transparent 0,transparent 50%,hsla(0,0%,100%,.15) 0,hsla(0,0%,100%,.15) 75%,transparent 0,transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:progress-bar-stripes 1s linear infinite}@media(prefers-reduced-motion:reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color:var(--bs-body-color);--bs-list-group-bg:var(--bs-body-bg);--bs-list-group-border-color:var(--bs-border-color);--bs-list-group-border-width:var(--bs-border-width);--bs-list-group-border-radius:var(--bs-border-radius);--bs-list-group-item-padding-x:1rem;--bs-list-group-item-padding-y:0.5rem;--bs-list-group-action-color:var(--bs-secondary-color);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-tertiary-bg);--bs-list-group-action-active-color:var(--bs-body-color);--bs-list-group-action-active-bg:var(--bs-secondary-bg);--bs-list-group-disabled-color:var(--bs-secondary-color);--bs-list-group-disabled-bg:var(--bs-body-bg);--bs-list-group-active-color:#fff;--bs-list-group-active-bg:#0d6efd;--bs-list-group-active-border-color:#0d6efd;border-radius:var(--bs-list-group-border-radius);display:flex;flex-direction:column;margin-bottom:0;padding-left:0}.list-group-numbered{counter-reset:section;list-style-type:none}.list-group-numbered>.list-group-item:before{content:counters(section,".") ". ";counter-increment:section}.list-group-item-action{color:var(--bs-list-group-action-color);text-align:inherit;width:100%}.list-group-item-action:focus,.list-group-item-action:hover{background-color:var(--bs-list-group-action-hover-bg);color:var(--bs-list-group-action-hover-color);text-decoration:none;z-index:1}.list-group-item-action:active{background-color:var(--bs-list-group-action-active-bg);color:var(--bs-list-group-action-active-color)}.list-group-item{background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color);color:var(--bs-list-group-color);display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);position:relative;text-decoration:none}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-left-radius:inherit;border-bottom-right-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{background-color:var(--bs-list-group-disabled-bg);color:var(--bs-list-group-disabled-color);pointer-events:none}.list-group-item.active{background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color);color:var(--bs-list-group-active-color);z-index:2}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{border-top-width:var(--bs-list-group-border-width);margin-top:calc(var(--bs-list-group-border-width)*-1)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}@media(min-width:540px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:720px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-md>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:960px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color:var(--bs-primary-text-emphasis);--bs-list-group-bg:var(--bs-primary-bg-subtle);--bs-list-group-border-color:var(--bs-primary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-primary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-primary-border-subtle);--bs-list-group-active-color:var(--bs-primary-bg-subtle);--bs-list-group-active-bg:var(--bs-primary-text-emphasis);--bs-list-group-active-border-color:var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color:var(--bs-secondary-text-emphasis);--bs-list-group-bg:var(--bs-secondary-bg-subtle);--bs-list-group-border-color:var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-secondary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-secondary-border-subtle);--bs-list-group-active-color:var(--bs-secondary-bg-subtle);--bs-list-group-active-bg:var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color:var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color:var(--bs-success-text-emphasis);--bs-list-group-bg:var(--bs-success-bg-subtle);--bs-list-group-border-color:var(--bs-success-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-success-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-success-border-subtle);--bs-list-group-active-color:var(--bs-success-bg-subtle);--bs-list-group-active-bg:var(--bs-success-text-emphasis);--bs-list-group-active-border-color:var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color:var(--bs-info-text-emphasis);--bs-list-group-bg:var(--bs-info-bg-subtle);--bs-list-group-border-color:var(--bs-info-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-info-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-info-border-subtle);--bs-list-group-active-color:var(--bs-info-bg-subtle);--bs-list-group-active-bg:var(--bs-info-text-emphasis);--bs-list-group-active-border-color:var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color:var(--bs-warning-text-emphasis);--bs-list-group-bg:var(--bs-warning-bg-subtle);--bs-list-group-border-color:var(--bs-warning-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-warning-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-warning-border-subtle);--bs-list-group-active-color:var(--bs-warning-bg-subtle);--bs-list-group-active-bg:var(--bs-warning-text-emphasis);--bs-list-group-active-border-color:var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color:var(--bs-danger-text-emphasis);--bs-list-group-bg:var(--bs-danger-bg-subtle);--bs-list-group-border-color:var(--bs-danger-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-danger-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-danger-border-subtle);--bs-list-group-active-color:var(--bs-danger-bg-subtle);--bs-list-group-active-bg:var(--bs-danger-text-emphasis);--bs-list-group-active-border-color:var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color:var(--bs-light-text-emphasis);--bs-list-group-bg:var(--bs-light-bg-subtle);--bs-list-group-border-color:var(--bs-light-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-light-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-light-border-subtle);--bs-list-group-active-color:var(--bs-light-bg-subtle);--bs-list-group-active-bg:var(--bs-light-text-emphasis);--bs-list-group-active-border-color:var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color:var(--bs-dark-text-emphasis);--bs-list-group-bg:var(--bs-dark-bg-subtle);--bs-list-group-border-color:var(--bs-dark-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-dark-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-dark-border-subtle);--bs-list-group-active-color:var(--bs-dark-bg-subtle);--bs-list-group-active-bg:var(--bs-dark-text-emphasis);--bs-list-group-active-border-color:var(--bs-dark-text-emphasis)}.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0)}.dropdown-item:hover,.dropdown-item:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active,.dropdown-item:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled,.dropdown-item:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:rgba(0,0,0,0)}.dropdown-menu.show{display:block}.dropdown-header{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:0.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: var(--pst-color-surface);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: var(--pst-color-surface);--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;flex-wrap:wrap;justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width)*-1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n+3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;align-items:flex-start;justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width)*-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: 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1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25rem;--bs-navbar-toggler-padding-x: 0.75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2833, 37, 41, 0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.offcanvas-body{display:flex;flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: rgba(255, 255, 255, 0.55);--bs-navbar-hover-color: rgba(255, 255, 255, 0.75);--bs-navbar-disabled-color: rgba(255, 255, 255, 0.25);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(255, 255, 255, 0.1);--bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 540px){.card-group{display:flex;flex-flow:row wrap}.card-group>.card{flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23052c65'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");--bs-accordion-btn-focus-border-color: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:\"\";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");--bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, \"/\") /* rtl: var(--bs-breadcrumb-divider, \"/\") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width)*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;flex-direction:column;justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: 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var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 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960px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e\");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto 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calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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!important}.pt-lg-0{padding-top:0 !important}.pt-lg-1{padding-top:.25rem !important}.pt-lg-2{padding-top:.5rem !important}.pt-lg-3{padding-top:1rem !important}.pt-lg-4{padding-top:1.5rem !important}.pt-lg-5{padding-top:3rem !important}.pe-lg-0{padding-right:0 !important}.pe-lg-1{padding-right:.25rem !important}.pe-lg-2{padding-right:.5rem !important}.pe-lg-3{padding-right:1rem !important}.pe-lg-4{padding-right:1.5rem !important}.pe-lg-5{padding-right:3rem !important}.pb-lg-0{padding-bottom:0 !important}.pb-lg-1{padding-bottom:.25rem !important}.pb-lg-2{padding-bottom:.5rem !important}.pb-lg-3{padding-bottom:1rem !important}.pb-lg-4{padding-bottom:1.5rem !important}.pb-lg-5{padding-bottom:3rem !important}.ps-lg-0{padding-left:0 !important}.ps-lg-1{padding-left:.25rem !important}.ps-lg-2{padding-left:.5rem !important}.ps-lg-3{padding-left:1rem !important}.ps-lg-4{padding-left:1.5rem !important}.ps-lg-5{padding-left:3rem !important}.gap-lg-0{gap:0 !important}.gap-lg-1{gap:.25rem !important}.gap-lg-2{gap:.5rem !important}.gap-lg-3{gap:1rem !important}.gap-lg-4{gap:1.5rem !important}.gap-lg-5{gap:3rem !important}.row-gap-lg-0{row-gap:0 !important}.row-gap-lg-1{row-gap:.25rem !important}.row-gap-lg-2{row-gap:.5rem !important}.row-gap-lg-3{row-gap:1rem !important}.row-gap-lg-4{row-gap:1.5rem !important}.row-gap-lg-5{row-gap:3rem !important}.column-gap-lg-0{column-gap:0 !important}.column-gap-lg-1{column-gap:.25rem !important}.column-gap-lg-2{column-gap:.5rem !important}.column-gap-lg-3{column-gap:1rem !important}.column-gap-lg-4{column-gap:1.5rem !important}.column-gap-lg-5{column-gap:3rem !important}.text-lg-start{text-align:left !important}.text-lg-end{text-align:right !important}.text-lg-center{text-align:center !important}}@media(min-width: 1200px){.float-xl-start{float:left !important}.float-xl-end{float:right !important}.float-xl-none{float:none !important}.object-fit-xl-contain{object-fit:contain !important}.object-fit-xl-cover{object-fit:cover !important}.object-fit-xl-fill{object-fit:fill !important}.object-fit-xl-scale{object-fit:scale-down !important}.object-fit-xl-none{object-fit:none !important}.d-xl-inline{display:inline !important}.d-xl-inline-block{display:inline-block !important}.d-xl-block{display:block !important}.d-xl-grid{display:grid !important}.d-xl-inline-grid{display:inline-grid !important}.d-xl-table{display:table !important}.d-xl-table-row{display:table-row !important}.d-xl-table-cell{display:table-cell !important}.d-xl-flex{display:flex !important}.d-xl-inline-flex{display:inline-flex !important}.d-xl-none{display:none !important}.flex-xl-fill{flex:1 1 auto !important}.flex-xl-row{flex-direction:row !important}.flex-xl-column{flex-direction:column !important}.flex-xl-row-reverse{flex-direction:row-reverse !important}.flex-xl-column-reverse{flex-direction:column-reverse !important}.flex-xl-grow-0{flex-grow:0 !important}.flex-xl-grow-1{flex-grow:1 !important}.flex-xl-shrink-0{flex-shrink:0 !important}.flex-xl-shrink-1{flex-shrink:1 !important}.flex-xl-wrap{flex-wrap:wrap !important}.flex-xl-nowrap{flex-wrap:nowrap !important}.flex-xl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xl-start{justify-content:flex-start !important}.justify-content-xl-end{justify-content:flex-end !important}.justify-content-xl-center{justify-content:center !important}.justify-content-xl-between{justify-content:space-between !important}.justify-content-xl-around{justify-content:space-around !important}.justify-content-xl-evenly{justify-content:space-evenly !important}.align-items-xl-start{align-items:flex-start !important}.align-items-xl-end{align-items:flex-end !important}.align-items-xl-center{align-items:center !important}.align-items-xl-baseline{align-items:baseline !important}.align-items-xl-stretch{align-items:stretch !important}.align-content-xl-start{align-content:flex-start !important}.align-content-xl-end{align-content:flex-end !important}.align-content-xl-center{align-content:center !important}.align-content-xl-between{align-content:space-between !important}.align-content-xl-around{align-content:space-around !important}.align-content-xl-stretch{align-content:stretch !important}.align-self-xl-auto{align-self:auto !important}.align-self-xl-start{align-self:flex-start !important}.align-self-xl-end{align-self:flex-end !important}.align-self-xl-center{align-self:center !important}.align-self-xl-baseline{align-self:baseline !important}.align-self-xl-stretch{align-self:stretch !important}.order-xl-first{order:-1 !important}.order-xl-0{order:0 !important}.order-xl-1{order:1 !important}.order-xl-2{order:2 !important}.order-xl-3{order:3 !important}.order-xl-4{order:4 !important}.order-xl-5{order:5 !important}.order-xl-last{order:6 !important}.m-xl-0{margin:0 !important}.m-xl-1{margin:.25rem !important}.m-xl-2{margin:.5rem !important}.m-xl-3{margin:1rem !important}.m-xl-4{margin:1.5rem !important}.m-xl-5{margin:3rem !important}.m-xl-auto{margin:auto !important}.mx-xl-0{margin-right:0 !important;margin-left:0 !important}.mx-xl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xl-auto{margin-right:auto !important;margin-left:auto !important}.my-xl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xl-0{margin-top:0 !important}.mt-xl-1{margin-top:.25rem !important}.mt-xl-2{margin-top:.5rem !important}.mt-xl-3{margin-top:1rem !important}.mt-xl-4{margin-top:1.5rem !important}.mt-xl-5{margin-top:3rem !important}.mt-xl-auto{margin-top:auto !important}.me-xl-0{margin-right:0 !important}.me-xl-1{margin-right:.25rem !important}.me-xl-2{margin-right:.5rem !important}.me-xl-3{margin-right:1rem !important}.me-xl-4{margin-right:1.5rem !important}.me-xl-5{margin-right:3rem !important}.me-xl-auto{margin-right:auto !important}.mb-xl-0{margin-bottom:0 !important}.mb-xl-1{margin-bottom:.25rem !important}.mb-xl-2{margin-bottom:.5rem !important}.mb-xl-3{margin-bottom:1rem !important}.mb-xl-4{margin-bottom:1.5rem !important}.mb-xl-5{margin-bottom:3rem !important}.mb-xl-auto{margin-bottom:auto !important}.ms-xl-0{margin-left:0 !important}.ms-xl-1{margin-left:.25rem !important}.ms-xl-2{margin-left:.5rem !important}.ms-xl-3{margin-left:1rem !important}.ms-xl-4{margin-left:1.5rem !important}.ms-xl-5{margin-left:3rem !important}.ms-xl-auto{margin-left:auto !important}.p-xl-0{padding:0 !important}.p-xl-1{padding:.25rem !important}.p-xl-2{padding:.5rem !important}.p-xl-3{padding:1rem !important}.p-xl-4{padding:1.5rem !important}.p-xl-5{padding:3rem !important}.px-xl-0{padding-right:0 !important;padding-left:0 !important}.px-xl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xl-0{padding-top:0 !important}.pt-xl-1{padding-top:.25rem !important}.pt-xl-2{padding-top:.5rem !important}.pt-xl-3{padding-top:1rem !important}.pt-xl-4{padding-top:1.5rem !important}.pt-xl-5{padding-top:3rem !important}.pe-xl-0{padding-right:0 !important}.pe-xl-1{padding-right:.25rem !important}.pe-xl-2{padding-right:.5rem !important}.pe-xl-3{padding-right:1rem !important}.pe-xl-4{padding-right:1.5rem !important}.pe-xl-5{padding-right:3rem !important}.pb-xl-0{padding-bottom:0 !important}.pb-xl-1{padding-bottom:.25rem !important}.pb-xl-2{padding-bottom:.5rem !important}.pb-xl-3{padding-bottom:1rem !important}.pb-xl-4{padding-bottom:1.5rem !important}.pb-xl-5{padding-bottom:3rem !important}.ps-xl-0{padding-left:0 !important}.ps-xl-1{padding-left:.25rem !important}.ps-xl-2{padding-left:.5rem !important}.ps-xl-3{padding-left:1rem !important}.ps-xl-4{padding-left:1.5rem !important}.ps-xl-5{padding-left:3rem !important}.gap-xl-0{gap:0 !important}.gap-xl-1{gap:.25rem !important}.gap-xl-2{gap:.5rem !important}.gap-xl-3{gap:1rem !important}.gap-xl-4{gap:1.5rem !important}.gap-xl-5{gap:3rem !important}.row-gap-xl-0{row-gap:0 !important}.row-gap-xl-1{row-gap:.25rem !important}.row-gap-xl-2{row-gap:.5rem !important}.row-gap-xl-3{row-gap:1rem !important}.row-gap-xl-4{row-gap:1.5rem !important}.row-gap-xl-5{row-gap:3rem !important}.column-gap-xl-0{column-gap:0 !important}.column-gap-xl-1{column-gap:.25rem !important}.column-gap-xl-2{column-gap:.5rem !important}.column-gap-xl-3{column-gap:1rem !important}.column-gap-xl-4{column-gap:1.5rem !important}.column-gap-xl-5{column-gap:3rem !important}.text-xl-start{text-align:left !important}.text-xl-end{text-align:right !important}.text-xl-center{text-align:center !important}}@media(min-width: 1200px){.fs-1{font-size:2.5rem !important}.fs-2{font-size:2rem !important}.fs-3{font-size:1.75rem !important}.fs-4{font-size:1.5rem !important}}@media print{.d-print-inline{display:inline !important}.d-print-inline-block{display:inline-block !important}.d-print-block{display:block !important}.d-print-grid{display:grid !important}.d-print-inline-grid{display:inline-grid !important}.d-print-table{display:table !important}.d-print-table-row{display:table-row !important}.d-print-table-cell{display:table-cell !important}.d-print-flex{display:flex !important}.d-print-inline-flex{display:inline-flex !important}.d-print-none{display:none !important}}","@mixin bsBanner($file) {\n  /*!\n   * Bootstrap #{$file} v5.3.2 (https://getbootstrap.com/)\n   * Copyright 2011-2023 The Bootstrap Authors\n   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n   */\n}\n",":root,\n[data-bs-theme=\"light\"] {\n  // Note: Custom variable values only support SassScript inside `#{}`.\n\n  // Colors\n  //\n  // Generate palettes for full colors, grays, and theme colors.\n\n  @each $color, $value in $colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $grays {\n    --#{$prefix}gray-#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-rgb {\n    --#{$prefix}#{$color}-rgb: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-text {\n    --#{$prefix}#{$color}-text-emphasis: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-bg-subtle {\n    --#{$prefix}#{$color}-bg-subtle: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-border-subtle {\n    --#{$prefix}#{$color}-border-subtle: #{$value};\n  }\n\n  --#{$prefix}white-rgb: #{to-rgb($white)};\n  --#{$prefix}black-rgb: #{to-rgb($black)};\n\n  // Fonts\n\n  // Note: Use `inspect` for lists so that quoted items keep the quotes.\n  // See https://github.com/sass/sass/issues/2383#issuecomment-336349172\n  --#{$prefix}font-sans-serif: #{inspect($font-family-sans-serif)};\n  --#{$prefix}font-monospace: #{inspect($font-family-monospace)};\n  --#{$prefix}gradient: #{$gradient};\n\n  // Root and body\n  // scss-docs-start root-body-variables\n  @if $font-size-root != null {\n    --#{$prefix}root-font-size: #{$font-size-root};\n  }\n  --#{$prefix}body-font-family: #{inspect($font-family-base)};\n  @include rfs($font-size-base, --#{$prefix}body-font-size);\n  --#{$prefix}body-font-weight: #{$font-weight-base};\n  --#{$prefix}body-line-height: #{$line-height-base};\n  @if $body-text-align != null {\n    --#{$prefix}body-text-align: #{$body-text-align};\n  }\n\n  --#{$prefix}body-color: #{$body-color};\n  --#{$prefix}body-color-rgb: #{to-rgb($body-color)};\n  --#{$prefix}body-bg: #{$body-bg};\n  --#{$prefix}body-bg-rgb: #{to-rgb($body-bg)};\n\n  --#{$prefix}emphasis-color: #{$body-emphasis-color};\n  --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color)};\n\n  --#{$prefix}secondary-color: #{$body-secondary-color};\n  --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color)};\n  --#{$prefix}secondary-bg: #{$body-secondary-bg};\n  --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg)};\n\n  --#{$prefix}tertiary-color: #{$body-tertiary-color};\n  --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color)};\n  --#{$prefix}tertiary-bg: #{$body-tertiary-bg};\n  --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg)};\n  // scss-docs-end root-body-variables\n\n  --#{$prefix}heading-color: #{$headings-color};\n\n  --#{$prefix}link-color: #{$link-color};\n  --#{$prefix}link-color-rgb: #{to-rgb($link-color)};\n  --#{$prefix}link-decoration: #{$link-decoration};\n\n  --#{$prefix}link-hover-color: #{$link-hover-color};\n  --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color)};\n\n  @if $link-hover-decoration != null {\n    --#{$prefix}link-hover-decoration: #{$link-hover-decoration};\n  }\n\n  --#{$prefix}code-color: #{$code-color};\n  --#{$prefix}highlight-color: #{$mark-color};\n  --#{$prefix}highlight-bg: #{$mark-bg};\n\n  // scss-docs-start root-border-var\n  --#{$prefix}border-width: #{$border-width};\n  --#{$prefix}border-style: #{$border-style};\n  --#{$prefix}border-color: #{$border-color};\n  --#{$prefix}border-color-translucent: #{$border-color-translucent};\n\n  --#{$prefix}border-radius: #{$border-radius};\n  --#{$prefix}border-radius-sm: #{$border-radius-sm};\n  --#{$prefix}border-radius-lg: #{$border-radius-lg};\n  --#{$prefix}border-radius-xl: #{$border-radius-xl};\n  --#{$prefix}border-radius-xxl: #{$border-radius-xxl};\n  --#{$prefix}border-radius-2xl: var(--#{$prefix}border-radius-xxl); // Deprecated in v5.3.0 for consistency\n  --#{$prefix}border-radius-pill: #{$border-radius-pill};\n  // scss-docs-end root-border-var\n\n  --#{$prefix}box-shadow: #{$box-shadow};\n  --#{$prefix}box-shadow-sm: #{$box-shadow-sm};\n  --#{$prefix}box-shadow-lg: #{$box-shadow-lg};\n  --#{$prefix}box-shadow-inset: #{$box-shadow-inset};\n\n  // Focus styles\n  // scss-docs-start root-focus-variables\n  --#{$prefix}focus-ring-width: #{$focus-ring-width};\n  --#{$prefix}focus-ring-opacity: #{$focus-ring-opacity};\n  --#{$prefix}focus-ring-color: #{$focus-ring-color};\n  // scss-docs-end root-focus-variables\n\n  // scss-docs-start root-form-validation-variables\n  --#{$prefix}form-valid-color: #{$form-valid-color};\n  --#{$prefix}form-valid-border-color: #{$form-valid-border-color};\n  --#{$prefix}form-invalid-color: #{$form-invalid-color};\n  --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color};\n  // scss-docs-end root-form-validation-variables\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark, true) {\n    color-scheme: dark;\n\n    // scss-docs-start root-dark-mode-vars\n    --#{$prefix}body-color: #{$body-color-dark};\n    --#{$prefix}body-color-rgb: #{to-rgb($body-color-dark)};\n    --#{$prefix}body-bg: #{$body-bg-dark};\n    --#{$prefix}body-bg-rgb: #{to-rgb($body-bg-dark)};\n\n    --#{$prefix}emphasis-color: #{$body-emphasis-color-dark};\n    --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color-dark)};\n\n    --#{$prefix}secondary-color: #{$body-secondary-color-dark};\n    --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color-dark)};\n    --#{$prefix}secondary-bg: #{$body-secondary-bg-dark};\n    --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg-dark)};\n\n    --#{$prefix}tertiary-color: #{$body-tertiary-color-dark};\n    --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color-dark)};\n    --#{$prefix}tertiary-bg: #{$body-tertiary-bg-dark};\n    --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg-dark)};\n\n    @each $color, $value in $theme-colors-text-dark {\n      --#{$prefix}#{$color}-text-emphasis: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-bg-subtle-dark {\n      --#{$prefix}#{$color}-bg-subtle: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-border-subtle-dark {\n      --#{$prefix}#{$color}-border-subtle: #{$value};\n    }\n\n    --#{$prefix}heading-color: #{$headings-color-dark};\n\n    --#{$prefix}link-color: #{$link-color-dark};\n    --#{$prefix}link-hover-color: #{$link-hover-color-dark};\n    --#{$prefix}link-color-rgb: #{to-rgb($link-color-dark)};\n    --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color-dark)};\n\n    --#{$prefix}code-color: #{$code-color-dark};\n    --#{$prefix}highlight-color: #{$mark-color-dark};\n    --#{$prefix}highlight-bg: #{$mark-bg-dark};\n\n    --#{$prefix}border-color: #{$border-color-dark};\n    --#{$prefix}border-color-translucent: #{$border-color-translucent-dark};\n\n    --#{$prefix}form-valid-color: #{$form-valid-color-dark};\n    --#{$prefix}form-valid-border-color: #{$form-valid-border-color-dark};\n    --#{$prefix}form-invalid-color: #{$form-invalid-color-dark};\n    --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color-dark};\n    // scss-docs-end root-dark-mode-vars\n  }\n}\n","// stylelint-disable scss/dimension-no-non-numeric-values\n\n// SCSS RFS mixin\n//\n// Automated responsive values for font sizes, paddings, margins and much more\n//\n// Licensed under MIT (https://github.com/twbs/rfs/blob/main/LICENSE)\n\n// Configuration\n\n// Base value\n$rfs-base-value: 1.25rem !default;\n$rfs-unit: rem !default;\n\n@if $rfs-unit != rem and $rfs-unit != px {\n  @error \"`#{$rfs-unit}` is not a valid unit for $rfs-unit. Use `px` or `rem`.\";\n}\n\n// Breakpoint at where values start decreasing if screen width is smaller\n$rfs-breakpoint: 1200px !default;\n$rfs-breakpoint-unit: px !default;\n\n@if $rfs-breakpoint-unit != px and $rfs-breakpoint-unit != em and $rfs-breakpoint-unit != rem {\n  @error \"`#{$rfs-breakpoint-unit}` is not a valid unit for $rfs-breakpoint-unit. Use `px`, `em` or `rem`.\";\n}\n\n// Resize values based on screen height and width\n$rfs-two-dimensional: false !default;\n\n// Factor of decrease\n$rfs-factor: 10 !default;\n\n@if type-of($rfs-factor) != number or $rfs-factor <= 1 {\n  @error \"`#{$rfs-factor}` is not a valid  $rfs-factor, it must be greater than 1.\";\n}\n\n// Mode. Possibilities: \"min-media-query\", \"max-media-query\"\n$rfs-mode: min-media-query !default;\n\n// Generate enable or disable classes. Possibilities: false, \"enable\" or \"disable\"\n$rfs-class: false !default;\n\n// 1 rem = $rfs-rem-value px\n$rfs-rem-value: 16 !default;\n\n// Safari iframe resize bug: https://github.com/twbs/rfs/issues/14\n$rfs-safari-iframe-resize-bug-fix: false !default;\n\n// Disable RFS by setting $enable-rfs to false\n$enable-rfs: true !default;\n\n// Cache $rfs-base-value unit\n$rfs-base-value-unit: unit($rfs-base-value);\n\n@function divide($dividend, $divisor, $precision: 10) {\n  $sign: if($dividend > 0 and $divisor > 0 or $dividend < 0 and $divisor < 0, 1, -1);\n  $dividend: abs($dividend);\n  $divisor: abs($divisor);\n  @if $dividend == 0 {\n    @return 0;\n  }\n  @if $divisor == 0 {\n    @error \"Cannot divide by 0\";\n  }\n  $remainder: $dividend;\n  $result: 0;\n  $factor: 10;\n  @while ($remainder > 0 and $precision >= 0) {\n    $quotient: 0;\n    @while ($remainder >= $divisor) {\n      $remainder: $remainder - $divisor;\n      $quotient: $quotient + 1;\n    }\n    $result: $result * 10 + $quotient;\n    $factor: $factor * .1;\n    $remainder: $remainder * 10;\n    $precision: $precision - 1;\n    @if ($precision < 0 and $remainder >= $divisor * 5) {\n      $result: $result + 1;\n    }\n  }\n  $result: $result * $factor * $sign;\n  $dividend-unit: unit($dividend);\n  $divisor-unit: unit($divisor);\n  $unit-map: (\n    \"px\": 1px,\n    \"rem\": 1rem,\n    \"em\": 1em,\n    \"%\": 1%\n  );\n  @if ($dividend-unit != $divisor-unit and map-has-key($unit-map, $dividend-unit)) {\n    $result: $result * map-get($unit-map, $dividend-unit);\n  }\n  @return $result;\n}\n\n// Remove px-unit from $rfs-base-value for calculations\n@if $rfs-base-value-unit == px {\n  $rfs-base-value: divide($rfs-base-value, $rfs-base-value * 0 + 1);\n}\n@else if $rfs-base-value-unit == rem {\n  $rfs-base-value: divide($rfs-base-value, divide($rfs-base-value * 0 + 1, $rfs-rem-value));\n}\n\n// Cache $rfs-breakpoint unit to prevent multiple calls\n$rfs-breakpoint-unit-cache: unit($rfs-breakpoint);\n\n// Remove unit from $rfs-breakpoint for calculations\n@if $rfs-breakpoint-unit-cache == px {\n  $rfs-breakpoint: divide($rfs-breakpoint, $rfs-breakpoint * 0 + 1);\n}\n@else if $rfs-breakpoint-unit-cache == rem or $rfs-breakpoint-unit-cache == \"em\" {\n  $rfs-breakpoint: divide($rfs-breakpoint, divide($rfs-breakpoint * 0 + 1, $rfs-rem-value));\n}\n\n// Calculate the media query value\n$rfs-mq-value: if($rfs-breakpoint-unit == px, #{$rfs-breakpoint}px, #{divide($rfs-breakpoint, $rfs-rem-value)}#{$rfs-breakpoint-unit});\n$rfs-mq-property-width: if($rfs-mode == max-media-query, max-width, min-width);\n$rfs-mq-property-height: if($rfs-mode == max-media-query, max-height, min-height);\n\n// Internal mixin used to determine which media query needs to be used\n@mixin _rfs-media-query {\n  @if $rfs-two-dimensional {\n    @if $rfs-mode == max-media-query {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}), (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n    @else {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) and (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n  }\n  @else {\n    @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) {\n      @content;\n    }\n  }\n}\n\n// Internal mixin that adds disable classes to the selector if needed.\n@mixin _rfs-rule {\n  @if $rfs-class == disable and $rfs-mode == max-media-query {\n    // Adding an extra class increases specificity, which prevents the media query to override the property\n    &,\n    .disable-rfs &,\n    &.disable-rfs {\n      @content;\n    }\n  }\n  @else if $rfs-class == enable and $rfs-mode == min-media-query {\n    .enable-rfs &,\n    &.enable-rfs {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Internal mixin that adds enable classes to the selector if needed.\n@mixin _rfs-media-query-rule {\n\n  @if $rfs-class == enable {\n    @if $rfs-mode == min-media-query {\n      @content;\n    }\n\n    @include _rfs-media-query () {\n      .enable-rfs &,\n      &.enable-rfs {\n        @content;\n      }\n    }\n  }\n  @else {\n    @if $rfs-class == disable and $rfs-mode == min-media-query {\n      .disable-rfs &,\n      &.disable-rfs {\n        @content;\n      }\n    }\n    @include _rfs-media-query () {\n      @content;\n    }\n  }\n}\n\n// Helper function to get the formatted non-responsive value\n@function rfs-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    }\n    @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      @if $unit == px {\n        // Convert to rem if needed\n        $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $value * 0 + $rfs-rem-value)}rem, $value);\n      }\n      @else if $unit == rem {\n        // Convert to px if needed\n        $val: $val + \" \" + if($rfs-unit == px, #{divide($value, $value * 0 + 1) * $rfs-rem-value}px, $value);\n      } @else {\n        // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n        $val: $val + \" \" + $value;\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// Helper function to get the responsive value calculated by RFS\n@function rfs-fluid-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    } @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n      @if not $unit or $unit != px and $unit != rem {\n        $val: $val + \" \" + $value;\n      } @else {\n        // Remove unit from $value for calculations\n        $value: divide($value, $value * 0 + if($unit == px, 1, divide(1, $rfs-rem-value)));\n\n        // Only add the media query if the value is greater than the minimum value\n        @if abs($value) <= $rfs-base-value or not $enable-rfs {\n          $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $rfs-rem-value)}rem, #{$value}px);\n        }\n        @else {\n          // Calculate the minimum value\n          $value-min: $rfs-base-value + divide(abs($value) - $rfs-base-value, $rfs-factor);\n\n          // Calculate difference between $value and the minimum value\n          $value-diff: abs($value) - $value-min;\n\n          // Base value formatting\n          $min-width: if($rfs-unit == rem, #{divide($value-min, $rfs-rem-value)}rem, #{$value-min}px);\n\n          // Use negative value if needed\n          $min-width: if($value < 0, -$min-width, $min-width);\n\n          // Use `vmin` if two-dimensional is enabled\n          $variable-unit: if($rfs-two-dimensional, vmin, vw);\n\n          // Calculate the variable width between 0 and $rfs-breakpoint\n          $variable-width: #{divide($value-diff * 100, $rfs-breakpoint)}#{$variable-unit};\n\n          // Return the calculated value\n          $val: $val + \" calc(\" + $min-width + if($value < 0, \" - \", \" + \") + $variable-width + \")\";\n        }\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// RFS mixin\n@mixin rfs($values, $property: font-size) {\n  @if $values != null {\n    $val: rfs-value($values);\n    $fluid-val: rfs-fluid-value($values);\n\n    // Do not print the media query if responsive & non-responsive values are the same\n    @if $val == $fluid-val {\n      #{$property}: $val;\n    }\n    @else {\n      @include _rfs-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $val, $fluid-val);\n\n        // Include safari iframe resize fix if needed\n        min-width: if($rfs-safari-iframe-resize-bug-fix, (0 * 1vw), null);\n      }\n\n      @include _rfs-media-query-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $fluid-val, $val);\n      }\n    }\n  }\n}\n\n// Shorthand helper mixins\n@mixin font-size($value) {\n  @include rfs($value);\n}\n\n@mixin padding($value) {\n  @include rfs($value, padding);\n}\n\n@mixin padding-top($value) {\n  @include rfs($value, padding-top);\n}\n\n@mixin padding-right($value) {\n  @include rfs($value, padding-right);\n}\n\n@mixin padding-bottom($value) {\n  @include rfs($value, padding-bottom);\n}\n\n@mixin padding-left($value) {\n  @include rfs($value, padding-left);\n}\n\n@mixin margin($value) {\n  @include rfs($value, margin);\n}\n\n@mixin margin-top($value) {\n  @include rfs($value, margin-top);\n}\n\n@mixin margin-right($value) {\n  @include rfs($value, margin-right);\n}\n\n@mixin margin-bottom($value) {\n  @include rfs($value, margin-bottom);\n}\n\n@mixin margin-left($value) {\n  @include rfs($value, margin-left);\n}\n","// scss-docs-start color-mode-mixin\n@mixin color-mode($mode: light, $root: false) {\n  @if $color-mode-type == \"media-query\" {\n    @if $root == true {\n      @media (prefers-color-scheme: $mode) {\n        :root {\n          @content;\n        }\n      }\n    } @else {\n      @media (prefers-color-scheme: $mode) {\n        @content;\n      }\n    }\n  } @else {\n    [data-bs-theme=\"#{$mode}\"] {\n      @content;\n    }\n  }\n}\n// scss-docs-end color-mode-mixin\n","// stylelint-disable declaration-no-important, selector-no-qualifying-type, property-no-vendor-prefix\n\n\n// Reboot\n//\n// Normalization of HTML elements, manually forked from Normalize.css to remove\n// styles targeting irrelevant browsers while applying new styles.\n//\n// Normalize is licensed MIT. https://github.com/necolas/normalize.css\n\n\n// Document\n//\n// Change from `box-sizing: content-box` so that `width` is not affected by `padding` or `border`.\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n}\n\n\n// Root\n//\n// Ability to the value of the root font sizes, affecting the value of `rem`.\n// null by default, thus nothing is generated.\n\n:root {\n  @if $font-size-root != null {\n    @include font-size(var(--#{$prefix}root-font-size));\n  }\n\n  @if $enable-smooth-scroll {\n    @media (prefers-reduced-motion: no-preference) {\n      scroll-behavior: smooth;\n    }\n  }\n}\n\n\n// Body\n//\n// 1. Remove the margin in all browsers.\n// 2. As a best practice, apply a default `background-color`.\n// 3. Prevent adjustments of font size after orientation changes in iOS.\n// 4. Change the default tap highlight to be completely transparent in iOS.\n\n// scss-docs-start reboot-body-rules\nbody {\n  margin: 0; // 1\n  font-family: var(--#{$prefix}body-font-family);\n  @include font-size(var(--#{$prefix}body-font-size));\n  font-weight: var(--#{$prefix}body-font-weight);\n  line-height: var(--#{$prefix}body-line-height);\n  color: var(--#{$prefix}body-color);\n  text-align: var(--#{$prefix}body-text-align);\n  background-color: var(--#{$prefix}body-bg); // 2\n  -webkit-text-size-adjust: 100%; // 3\n  -webkit-tap-highlight-color: rgba($black, 0); // 4\n}\n// scss-docs-end reboot-body-rules\n\n\n// Content grouping\n//\n// 1. Reset Firefox's gray color\n\nhr {\n  margin: $hr-margin-y 0;\n  color: $hr-color; // 1\n  border: 0;\n  border-top: $hr-border-width solid $hr-border-color;\n  opacity: $hr-opacity;\n}\n\n\n// Typography\n//\n// 1. Remove top margins from headings\n//    By default, `<h1>`-`<h6>` all receive top and bottom margins. We nuke the top\n//    margin for easier control within type scales as it avoids margin collapsing.\n\n%heading {\n  margin-top: 0; // 1\n  margin-bottom: $headings-margin-bottom;\n  font-family: $headings-font-family;\n  font-style: $headings-font-style;\n  font-weight: $headings-font-weight;\n  line-height: $headings-line-height;\n  color: var(--#{$prefix}heading-color);\n}\n\nh1 {\n  @extend %heading;\n  @include font-size($h1-font-size);\n}\n\nh2 {\n  @extend %heading;\n  @include font-size($h2-font-size);\n}\n\nh3 {\n  @extend %heading;\n  @include font-size($h3-font-size);\n}\n\nh4 {\n  @extend %heading;\n  @include font-size($h4-font-size);\n}\n\nh5 {\n  @extend %heading;\n  @include font-size($h5-font-size);\n}\n\nh6 {\n  @extend %heading;\n  @include font-size($h6-font-size);\n}\n\n\n// Reset margins on paragraphs\n//\n// Similarly, the top margin on `<p>`s get reset. However, we also reset the\n// bottom margin to use `rem` units instead of `em`.\n\np {\n  margin-top: 0;\n  margin-bottom: $paragraph-margin-bottom;\n}\n\n\n// Abbreviations\n//\n// 1. Add the correct text decoration in Chrome, Edge, Opera, and Safari.\n// 2. Add explicit cursor to indicate changed behavior.\n// 3. Prevent the text-decoration to be skipped.\n\nabbr[title] {\n  text-decoration: underline dotted; // 1\n  cursor: help; // 2\n  text-decoration-skip-ink: none; // 3\n}\n\n\n// Address\n\naddress {\n  margin-bottom: 1rem;\n  font-style: normal;\n  line-height: inherit;\n}\n\n\n// Lists\n\nol,\nul {\n  padding-left: 2rem;\n}\n\nol,\nul,\ndl {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n  margin-bottom: 0;\n}\n\ndt {\n  font-weight: $dt-font-weight;\n}\n\n// 1. Undo browser default\n\ndd {\n  margin-bottom: .5rem;\n  margin-left: 0; // 1\n}\n\n\n// Blockquote\n\nblockquote {\n  margin: 0 0 1rem;\n}\n\n\n// Strong\n//\n// Add the correct font weight in Chrome, Edge, and Safari\n\nb,\nstrong {\n  font-weight: $font-weight-bolder;\n}\n\n\n// Small\n//\n// Add the correct font size in all browsers\n\nsmall {\n  @include font-size($small-font-size);\n}\n\n\n// Mark\n\nmark {\n  padding: $mark-padding;\n  color: var(--#{$prefix}highlight-color);\n  background-color: var(--#{$prefix}highlight-bg);\n}\n\n\n// Sub and Sup\n//\n// Prevent `sub` and `sup` elements from affecting the line height in\n// all browsers.\n\nsub,\nsup {\n  position: relative;\n  @include font-size($sub-sup-font-size);\n  line-height: 0;\n  vertical-align: baseline;\n}\n\nsub { bottom: -.25em; }\nsup { top: -.5em; }\n\n\n// Links\n\na {\n  color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, 1));\n  text-decoration: $link-decoration;\n\n  &:hover {\n    --#{$prefix}link-color-rgb: var(--#{$prefix}link-hover-color-rgb);\n    text-decoration: $link-hover-decoration;\n  }\n}\n\n// And undo these styles for placeholder links/named anchors (without href).\n// It would be more straightforward to just use a[href] in previous block, but that\n// causes specificity issues in many other styles that are too complex to fix.\n// See https://github.com/twbs/bootstrap/issues/19402\n\na:not([href]):not([class]) {\n  &,\n  &:hover {\n    color: inherit;\n    text-decoration: none;\n  }\n}\n\n\n// Code\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: $font-family-code;\n  @include font-size(1em); // Correct the odd `em` font sizing in all browsers.\n}\n\n// 1. Remove browser default top margin\n// 2. Reset browser default of `1em` to use `rem`s\n// 3. Don't allow content to break outside\n\npre {\n  display: block;\n  margin-top: 0; // 1\n  margin-bottom: 1rem; // 2\n  overflow: auto; // 3\n  @include font-size($code-font-size);\n  color: $pre-color;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    @include font-size(inherit);\n    color: inherit;\n    word-break: normal;\n  }\n}\n\ncode {\n  @include font-size($code-font-size);\n  color: var(--#{$prefix}code-color);\n  word-wrap: break-word;\n\n  // Streamline the style when inside anchors to avoid broken underline and more\n  a > & {\n    color: inherit;\n  }\n}\n\nkbd {\n  padding: $kbd-padding-y $kbd-padding-x;\n  @include font-size($kbd-font-size);\n  color: $kbd-color;\n  background-color: $kbd-bg;\n  @include border-radius($border-radius-sm);\n\n  kbd {\n    padding: 0;\n    @include font-size(1em);\n    font-weight: $nested-kbd-font-weight;\n  }\n}\n\n\n// Figures\n//\n// Apply a consistent margin strategy (matches our type styles).\n\nfigure {\n  margin: 0 0 1rem;\n}\n\n\n// Images and content\n\nimg,\nsvg {\n  vertical-align: middle;\n}\n\n\n// Tables\n//\n// Prevent double borders\n\ntable {\n  caption-side: bottom;\n  border-collapse: collapse;\n}\n\ncaption {\n  padding-top: $table-cell-padding-y;\n  padding-bottom: $table-cell-padding-y;\n  color: $table-caption-color;\n  text-align: left;\n}\n\n// 1. Removes font-weight bold by inheriting\n// 2. Matches default `<td>` alignment by inheriting `text-align`.\n// 3. Fix alignment for Safari\n\nth {\n  font-weight: $table-th-font-weight; // 1\n  text-align: inherit; // 2\n  text-align: -webkit-match-parent; // 3\n}\n\nthead,\ntbody,\ntfoot,\ntr,\ntd,\nth {\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n}\n\n\n// Forms\n//\n// 1. Allow labels to use `margin` for spacing.\n\nlabel {\n  display: inline-block; // 1\n}\n\n// Remove the default `border-radius` that macOS Chrome adds.\n// See https://github.com/twbs/bootstrap/issues/24093\n\nbutton {\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 0;\n}\n\n// Explicitly remove focus outline in Chromium when it shouldn't be\n// visible (e.g. as result of mouse click or touch tap). It already\n// should be doing this automatically, but seems to currently be\n// confused and applies its very visible two-tone outline anyway.\n\nbutton:focus:not(:focus-visible) {\n  outline: 0;\n}\n\n// 1. Remove the margin in Firefox and Safari\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n  margin: 0; // 1\n  font-family: inherit;\n  @include font-size(inherit);\n  line-height: inherit;\n}\n\n// Remove the inheritance of text transform in Firefox\nbutton,\nselect {\n  text-transform: none;\n}\n// Set the cursor for non-`<button>` buttons\n//\n// Details at https://github.com/twbs/bootstrap/pull/30562\n[role=\"button\"] {\n  cursor: pointer;\n}\n\nselect {\n  // Remove the inheritance of word-wrap in Safari.\n  // See https://github.com/twbs/bootstrap/issues/24990\n  word-wrap: normal;\n\n  // Undo the opacity change from Chrome\n  &:disabled {\n    opacity: 1;\n  }\n}\n\n// Remove the dropdown arrow only from text type inputs built with datalists in Chrome.\n// See https://stackoverflow.com/a/54997118\n\n[list]:not([type=\"date\"]):not([type=\"datetime-local\"]):not([type=\"month\"]):not([type=\"week\"]):not([type=\"time\"])::-webkit-calendar-picker-indicator {\n  display: none !important;\n}\n\n// 1. Prevent a WebKit bug where (2) destroys native `audio` and `video`\n//    controls in Android 4.\n// 2. Correct the inability to style clickable types in iOS and Safari.\n// 3. Opinionated: add \"hand\" cursor to non-disabled button elements.\n\nbutton,\n[type=\"button\"], // 1\n[type=\"reset\"],\n[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n\n  @if $enable-button-pointers {\n    &:not(:disabled) {\n      cursor: pointer; // 3\n    }\n  }\n}\n\n// Remove inner border and padding from Firefox, but don't restore the outline like Normalize.\n\n::-moz-focus-inner {\n  padding: 0;\n  border-style: none;\n}\n\n// 1. Textareas should really only resize vertically so they don't break their (horizontal) containers.\n\ntextarea {\n  resize: vertical; // 1\n}\n\n// 1. Browsers set a default `min-width: min-content;` on fieldsets,\n//    unlike e.g. `<div>`s, which have `min-width: 0;` by default.\n//    So we reset that to ensure fieldsets behave more like a standard block element.\n//    See https://github.com/twbs/bootstrap/issues/12359\n//    and https://html.spec.whatwg.org/multipage/#the-fieldset-and-legend-elements\n// 2. Reset the default outline behavior of fieldsets so they don't affect page layout.\n\nfieldset {\n  min-width: 0; // 1\n  padding: 0; // 2\n  margin: 0; // 2\n  border: 0; // 2\n}\n\n// 1. By using `float: left`, the legend will behave like a block element.\n//    This way the border of a fieldset wraps around the legend if present.\n// 2. Fix wrapping bug.\n//    See https://github.com/twbs/bootstrap/issues/29712\n\nlegend {\n  float: left; // 1\n  width: 100%;\n  padding: 0;\n  margin-bottom: $legend-margin-bottom;\n  @include font-size($legend-font-size);\n  font-weight: $legend-font-weight;\n  line-height: inherit;\n\n  + * {\n    clear: left; // 2\n  }\n}\n\n// Fix height of inputs with a type of datetime-local, date, month, week, or time\n// See https://github.com/twbs/bootstrap/issues/18842\n\n::-webkit-datetime-edit-fields-wrapper,\n::-webkit-datetime-edit-text,\n::-webkit-datetime-edit-minute,\n::-webkit-datetime-edit-hour-field,\n::-webkit-datetime-edit-day-field,\n::-webkit-datetime-edit-month-field,\n::-webkit-datetime-edit-year-field {\n  padding: 0;\n}\n\n::-webkit-inner-spin-button {\n  height: auto;\n}\n\n// 1. This overrides the extra rounded corners on search inputs in iOS so that our\n//    `.form-control` class can properly style them. Note that this cannot simply\n//    be added to `.form-control` as it's not specific enough. For details, see\n//    https://github.com/twbs/bootstrap/issues/11586.\n// 2. Correct the outline style in Safari.\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  outline-offset: -2px; // 2\n}\n\n// 1. A few input types should stay LTR\n// See https://rtlstyling.com/posts/rtl-styling#form-inputs\n// 2. RTL only output\n// See https://rtlcss.com/learn/usage-guide/control-directives/#raw\n\n/* rtl:raw:\n[type=\"tel\"],\n[type=\"url\"],\n[type=\"email\"],\n[type=\"number\"] {\n  direction: ltr;\n}\n*/\n\n// Remove the inner padding in Chrome and Safari on macOS.\n\n::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n// Remove padding around color pickers in webkit browsers\n\n::-webkit-color-swatch-wrapper {\n  padding: 0;\n}\n\n\n// 1. Inherit font family and line height for file input buttons\n// 2. Correct the inability to style clickable types in iOS and Safari.\n\n::file-selector-button {\n  font: inherit; // 1\n  -webkit-appearance: button; // 2\n}\n\n// Correct element displays\n\noutput {\n  display: inline-block;\n}\n\n// Remove border from iframe\n\niframe {\n  border: 0;\n}\n\n// Summary\n//\n// 1. Add the correct display in all browsers\n\nsummary {\n  display: list-item; // 1\n  cursor: pointer;\n}\n\n\n// Progress\n//\n// Add the correct vertical alignment in Chrome, Firefox, and Opera.\n\nprogress {\n  vertical-align: baseline;\n}\n\n\n// Hidden attribute\n//\n// Always hide an element with the `hidden` HTML attribute.\n\n[hidden] {\n  display: none !important;\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n$accordion-button-focus-border-color:     $input-focus-border-color !default;\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-active-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n","// stylelint-disable property-disallowed-list\n// Single side border-radius\n\n// Helper function to replace negative values with 0\n@function valid-radius($radius) {\n  $return: ();\n  @each $value in $radius {\n    @if type-of($value) == number {\n      $return: append($return, max($value, 0));\n    } @else {\n      $return: append($return, $value);\n    }\n  }\n  @return $return;\n}\n\n// scss-docs-start border-radius-mixins\n@mixin border-radius($radius: $border-radius, $fallback-border-radius: false) {\n  @if $enable-rounded {\n    border-radius: valid-radius($radius);\n  }\n  @else if $fallback-border-radius != false {\n    border-radius: $fallback-border-radius;\n  }\n}\n\n@mixin border-top-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n// scss-docs-end border-radius-mixins\n","//\n// Headings\n//\n.h1 {\n  @extend h1;\n}\n\n.h2 {\n  @extend h2;\n}\n\n.h3 {\n  @extend h3;\n}\n\n.h4 {\n  @extend h4;\n}\n\n.h5 {\n  @extend h5;\n}\n\n.h6 {\n  @extend h6;\n}\n\n\n.lead {\n  @include font-size($lead-font-size);\n  font-weight: $lead-font-weight;\n}\n\n// Type display classes\n@each $display, $font-size in $display-font-sizes {\n  .display-#{$display} {\n    @include font-size($font-size);\n    font-family: $display-font-family;\n    font-style: $display-font-style;\n    font-weight: $display-font-weight;\n    line-height: $display-line-height;\n  }\n}\n\n//\n// Emphasis\n//\n.small {\n  @extend small;\n}\n\n.mark {\n  @extend mark;\n}\n\n//\n// Lists\n//\n\n.list-unstyled {\n  @include list-unstyled();\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n  @include list-unstyled();\n}\n.list-inline-item {\n  display: inline-block;\n\n  &:not(:last-child) {\n    margin-right: $list-inline-padding;\n  }\n}\n\n\n//\n// Misc\n//\n\n// Builds on `abbr`\n.initialism {\n  @include font-size($initialism-font-size);\n  text-transform: uppercase;\n}\n\n// Blockquotes\n.blockquote {\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-font-size);\n\n  > :last-child {\n    margin-bottom: 0;\n  }\n}\n\n.blockquote-footer {\n  margin-top: -$blockquote-margin-y;\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-footer-font-size);\n  color: $blockquote-footer-color;\n\n  &::before {\n    content: \"\\2014\\00A0\"; // em dash, nbsp\n  }\n}\n","// Lists\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n@mixin list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n","// Override bootstrap variables\n$spacer: 1rem;\n\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1400px,\n);\n\n$grid-breakpoints: (\n  xs: 0,\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1200px,\n);\n\n$dropdown-link-hover-bg: var(--pst-color-surface);\n// --pst-color-surface can also be assigned to the dark variant because it is\n// scoped to different values depending on light/dark theme\n$dropdown-dark-link-hover-bg: var(--pst-color-surface);\n$dropdown-link-active-bg: var(--pst-color-surface);\n$dropdown-dark-link-active-bg: var(--pst-color-surface);\n","// Responsive images (ensure images don't scale beyond their parents)\n//\n// This is purposefully opt-in via an explicit class rather than being the default for all `<img>`s.\n// We previously tried the \"images are responsive by default\" approach in Bootstrap v2,\n// and abandoned it in Bootstrap v3 because it breaks lots of third-party widgets (including Google Maps)\n// which weren't expecting the images within themselves to be involuntarily resized.\n// See also https://github.com/twbs/bootstrap/issues/18178\n.img-fluid {\n  @include img-fluid();\n}\n\n\n// Image thumbnails\n.img-thumbnail {\n  padding: $thumbnail-padding;\n  background-color: $thumbnail-bg;\n  border: $thumbnail-border-width solid $thumbnail-border-color;\n  @include border-radius($thumbnail-border-radius);\n  @include box-shadow($thumbnail-box-shadow);\n\n  // Keep them at most 100% wide\n  @include img-fluid();\n}\n\n//\n// Figures\n//\n\n.figure {\n  // Ensures the caption's text aligns with the image.\n  display: inline-block;\n}\n\n.figure-img {\n  margin-bottom: $spacer * .5;\n  line-height: 1;\n}\n\n.figure-caption {\n  @include font-size($figure-caption-font-size);\n  color: $figure-caption-color;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n\n@mixin img-fluid {\n  // Part 1: Set a maximum relative to the parent\n  max-width: 100%;\n  // Part 2: Override the height to auto, otherwise images will be stretched\n  // when setting a width and height attribute on the img element.\n  height: auto;\n}\n","// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n@if $enable-container-classes {\n  // Single container class with breakpoint max-widths\n  .container,\n  // 100% wide container at all breakpoints\n  .container-fluid {\n    @include make-container();\n  }\n\n  // Responsive containers that are 100% wide until a breakpoint\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    .container-#{$breakpoint} {\n      @extend .container-fluid;\n    }\n\n    @include media-breakpoint-up($breakpoint, $grid-breakpoints) {\n      %responsive-container-#{$breakpoint} {\n        max-width: $container-max-width;\n      }\n\n      // Extend each breakpoint which is smaller or equal to the current breakpoint\n      $extend-breakpoint: true;\n\n      @each $name, $width in $grid-breakpoints {\n        @if ($extend-breakpoint) {\n          .container#{breakpoint-infix($name, $grid-breakpoints)} {\n            @extend %responsive-container-#{$breakpoint};\n          }\n\n          // Once the current breakpoint is reached, stop extending\n          @if ($breakpoint == $name) {\n            $extend-breakpoint: false;\n          }\n        }\n      }\n    }\n  }\n}\n","// Container mixins\n\n@mixin make-container($gutter: $container-padding-x) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  width: 100%;\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-right: auto;\n  margin-left: auto;\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n","// Row\n//\n// Rows contain your columns.\n\n:root {\n  @each $name, $value in $grid-breakpoints {\n    --#{$prefix}breakpoint-#{$name}: #{$value};\n  }\n}\n\n@if $enable-grid-classes {\n  .row {\n    @include make-row();\n\n    > * {\n      @include make-col-ready();\n    }\n  }\n}\n\n@if $enable-cssgrid {\n  .grid {\n    display: grid;\n    grid-template-rows: repeat(var(--#{$prefix}rows, 1), 1fr);\n    grid-template-columns: repeat(var(--#{$prefix}columns, #{$grid-columns}), 1fr);\n    gap: var(--#{$prefix}gap, #{$grid-gutter-width});\n\n    @include make-cssgrid();\n  }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n@if $enable-grid-classes {\n  @include make-grid-columns();\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is and invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Basic Bootstrap table\n//\n\n.table {\n  // Reset needed for nesting tables\n  --#{$prefix}table-color-type: initial;\n  --#{$prefix}table-bg-type: initial;\n  --#{$prefix}table-color-state: initial;\n  --#{$prefix}table-bg-state: initial;\n  // End of reset\n  --#{$prefix}table-color: #{$table-color};\n  --#{$prefix}table-bg: #{$table-bg};\n  --#{$prefix}table-border-color: #{$table-border-color};\n  --#{$prefix}table-accent-bg: #{$table-accent-bg};\n  --#{$prefix}table-striped-color: #{$table-striped-color};\n  --#{$prefix}table-striped-bg: #{$table-striped-bg};\n  --#{$prefix}table-active-color: #{$table-active-color};\n  --#{$prefix}table-active-bg: #{$table-active-bg};\n  --#{$prefix}table-hover-color: #{$table-hover-color};\n  --#{$prefix}table-hover-bg: #{$table-hover-bg};\n\n  width: 100%;\n  margin-bottom: $spacer;\n  vertical-align: $table-cell-vertical-align;\n  border-color: var(--#{$prefix}table-border-color);\n\n  // Target th & td\n  // We need the child combinator to prevent styles leaking to nested tables which doesn't have a `.table` class.\n  // We use the universal selectors here to simplify the selector (else we would need 6 different selectors).\n  // Another advantage is that this generates less code and makes the selector less specific making it easier to override.\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y $table-cell-padding-x;\n    // Following the precept of cascades: https://codepen.io/miriamsuzanne/full/vYNgodb\n    color: var(--#{$prefix}table-color-state, var(--#{$prefix}table-color-type, var(--#{$prefix}table-color)));\n    background-color: var(--#{$prefix}table-bg);\n    border-bottom-width: $table-border-width;\n    box-shadow: inset 0 0 0 9999px var(--#{$prefix}table-bg-state, var(--#{$prefix}table-bg-type, var(--#{$prefix}table-accent-bg)));\n  }\n\n  > tbody {\n    vertical-align: inherit;\n  }\n\n  > thead {\n    vertical-align: bottom;\n  }\n}\n\n.table-group-divider {\n  border-top: calc(#{$table-border-width} * 2) solid $table-group-separator-color; // stylelint-disable-line function-disallowed-list\n}\n\n//\n// Change placement of captions with a class\n//\n\n.caption-top {\n  caption-side: top;\n}\n\n\n//\n// Condensed table w/ half padding\n//\n\n.table-sm {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y-sm $table-cell-padding-x-sm;\n  }\n}\n\n\n// Border versions\n//\n// Add or remove borders all around the table and between all the columns.\n//\n// When borders are added on all sides of the cells, the corners can render odd when\n// these borders do not have the same color or if they are semi-transparent.\n// Therefor we add top and border bottoms to the `tr`s and left and right borders\n// to the `td`s or `th`s\n\n.table-bordered {\n  > :not(caption) > * {\n    border-width: $table-border-width 0;\n\n    // stylelint-disable-next-line selector-max-universal\n    > * {\n      border-width: 0 $table-border-width;\n    }\n  }\n}\n\n.table-borderless {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    border-bottom-width: 0;\n  }\n\n  > :not(:first-child) {\n    border-top-width: 0;\n  }\n}\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n// For rows\n.table-striped {\n  > tbody > tr:nth-of-type(#{$table-striped-order}) > * {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// For columns\n.table-striped-columns {\n  > :not(caption) > tr > :nth-child(#{$table-striped-columns-order}) {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// Active table\n//\n// The `.table-active` class can be added to highlight rows or cells\n\n.table-active {\n  --#{$prefix}table-color-state: var(--#{$prefix}table-active-color);\n  --#{$prefix}table-bg-state: var(--#{$prefix}table-active-bg);\n}\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover > * {\n    --#{$prefix}table-color-state: var(--#{$prefix}table-hover-color);\n    --#{$prefix}table-bg-state: var(--#{$prefix}table-hover-bg);\n  }\n}\n\n\n// Table variants\n//\n// Table variants set the table cell backgrounds, border colors\n// and the colors of the striped, hovered & active tables\n\n@each $color, $value in $table-variants {\n  @include table-variant($color, $value);\n}\n\n// Responsive tables\n//\n// Generate series of `.table-responsive-*` classes for configuring the screen\n// size of where your table will overflow.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n  @include media-breakpoint-down($breakpoint) {\n    .table-responsive#{$infix} {\n      overflow-x: auto;\n      -webkit-overflow-scrolling: touch;\n    }\n  }\n}\n","// scss-docs-start table-variant\n@mixin table-variant($state, $background) {\n  .table-#{$state} {\n    $color: color-contrast(opaque($body-bg, $background));\n    $hover-bg: mix($color, $background, percentage($table-hover-bg-factor));\n    $striped-bg: mix($color, $background, percentage($table-striped-bg-factor));\n    $active-bg: mix($color, $background, percentage($table-active-bg-factor));\n    $table-border-color: mix($color, $background, percentage($table-border-factor));\n\n    --#{$prefix}table-color: #{$color};\n    --#{$prefix}table-bg: #{$background};\n    --#{$prefix}table-border-color: #{$table-border-color};\n    --#{$prefix}table-striped-bg: #{$striped-bg};\n    --#{$prefix}table-striped-color: #{color-contrast($striped-bg)};\n    --#{$prefix}table-active-bg: #{$active-bg};\n    --#{$prefix}table-active-color: #{color-contrast($active-bg)};\n    --#{$prefix}table-hover-bg: #{$hover-bg};\n    --#{$prefix}table-hover-color: #{color-contrast($hover-bg)};\n\n    color: var(--#{$prefix}table-color);\n    border-color: var(--#{$prefix}table-border-color);\n  }\n}\n// scss-docs-end table-variant\n","//\n// Labels\n//\n\n.form-label {\n  margin-bottom: $form-label-margin-bottom;\n  @include font-size($form-label-font-size);\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  color: $form-label-color;\n}\n\n// For use with horizontal and inline forms, when you need the label (or legend)\n// text to align with the form controls.\n.col-form-label {\n  padding-top: add($input-padding-y, $input-border-width);\n  padding-bottom: add($input-padding-y, $input-border-width);\n  margin-bottom: 0; // Override the `<legend>` default\n  @include font-size(inherit); // Override the `<legend>` default\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  line-height: $input-line-height;\n  color: $form-label-color;\n}\n\n.col-form-label-lg {\n  padding-top: add($input-padding-y-lg, $input-border-width);\n  padding-bottom: add($input-padding-y-lg, $input-border-width);\n  @include font-size($input-font-size-lg);\n}\n\n.col-form-label-sm {\n  padding-top: add($input-padding-y-sm, $input-border-width);\n  padding-bottom: add($input-padding-y-sm, $input-border-width);\n  @include font-size($input-font-size-sm);\n}\n","//\n// Form text\n//\n\n.form-text {\n  margin-top: $form-text-margin-top;\n  @include font-size($form-text-font-size);\n  font-style: $form-text-font-style;\n  font-weight: $form-text-font-weight;\n  color: $form-text-color;\n}\n","//\n// General form controls (plus a few specific high-level interventions)\n//\n\n.form-control {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y $input-padding-x;\n  font-family: $input-font-family;\n  @include font-size($input-font-size);\n  font-weight: $input-font-weight;\n  line-height: $input-line-height;\n  color: $input-color;\n  appearance: none; // Fix appearance for date inputs in Safari\n  background-color: $input-bg;\n  background-clip: padding-box;\n  border: $input-border-width solid $input-border-color;\n\n  // Note: This has no effect on <select>s in some browsers, due to the limited stylability of `<select>`s in CSS.\n  @include border-radius($input-border-radius, 0);\n\n  @include box-shadow($input-box-shadow);\n  @include transition($input-transition);\n\n  &[type=\"file\"] {\n    overflow: hidden; // prevent pseudo element button overlap\n\n    &:not(:disabled):not([readonly]) {\n      cursor: pointer;\n    }\n  }\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  &:focus {\n    color: $input-focus-color;\n    background-color: $input-focus-bg;\n    border-color: $input-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($input-box-shadow, $input-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $input-focus-box-shadow;\n    }\n  }\n\n  &::-webkit-date-and-time-value {\n    // On Android Chrome, form-control's \"width: 100%\" makes the input width too small\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    //\n    // On iOS Safari, form-control's \"appearance: none\" + \"width: 100%\" makes the input width too small\n    // Tested under iOS 16.2 / Safari 16.2\n    min-width: 85px; // Seems to be a good minimum safe width\n\n    // Add some height to date inputs on iOS\n    // https://github.com/twbs/bootstrap/issues/23307\n    // TODO: we can remove this workaround once https://bugs.webkit.org/show_bug.cgi?id=198959 is resolved\n    // Multiply line-height by 1em if it has no unit\n    height: if(unit($input-line-height) == \"\", $input-line-height * 1em, $input-line-height);\n\n    // Android Chrome type=\"date\" is taller than the other inputs\n    // because of \"margin: 1px 24px 1px 4px\" inside the shadow DOM\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    margin: 0;\n  }\n\n  // Prevent excessive date input height in Webkit\n  // https://github.com/twbs/bootstrap/issues/34433\n  &::-webkit-datetime-edit {\n    display: block;\n    padding: 0;\n  }\n\n  // Placeholder\n  &::placeholder {\n    color: $input-placeholder-color;\n    // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526.\n    opacity: 1;\n  }\n\n  // Disabled inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &:disabled {\n    color: $input-disabled-color;\n    background-color: $input-disabled-bg;\n    border-color: $input-disabled-border-color;\n    // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655.\n    opacity: 1;\n  }\n\n  // File input buttons theming\n  &::file-selector-button {\n    padding: $input-padding-y $input-padding-x;\n    margin: (-$input-padding-y) (-$input-padding-x);\n    margin-inline-end: $input-padding-x;\n    color: $form-file-button-color;\n    @include gradient-bg($form-file-button-bg);\n    pointer-events: none;\n    border-color: inherit;\n    border-style: solid;\n    border-width: 0;\n    border-inline-end-width: $input-border-width;\n    border-radius: 0; // stylelint-disable-line property-disallowed-list\n    @include transition($btn-transition);\n  }\n\n  &:hover:not(:disabled):not([readonly])::file-selector-button {\n    background-color: $form-file-button-hover-bg;\n  }\n}\n\n// Readonly controls as plain text\n//\n// Apply class to a readonly input to make it appear like regular plain\n// text (without any border, background color, focus indicator)\n\n.form-control-plaintext {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y 0;\n  margin-bottom: 0; // match inputs if this class comes on inputs with default margins\n  line-height: $input-line-height;\n  color: $input-plaintext-color;\n  background-color: transparent;\n  border: solid transparent;\n  border-width: $input-border-width 0;\n\n  &:focus {\n    outline: 0;\n  }\n\n  &.form-control-sm,\n  &.form-control-lg {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// Repeated in `_input_group.scss` to avoid Sass extend issues.\n\n.form-control-sm {\n  min-height: $input-height-sm;\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n\n  &::file-selector-button {\n    padding: $input-padding-y-sm $input-padding-x-sm;\n    margin: (-$input-padding-y-sm) (-$input-padding-x-sm);\n    margin-inline-end: $input-padding-x-sm;\n  }\n}\n\n.form-control-lg {\n  min-height: $input-height-lg;\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n\n  &::file-selector-button {\n    padding: $input-padding-y-lg $input-padding-x-lg;\n    margin: (-$input-padding-y-lg) (-$input-padding-x-lg);\n    margin-inline-end: $input-padding-x-lg;\n  }\n}\n\n// Make sure textareas don't shrink too much when resized\n// https://github.com/twbs/bootstrap/pull/29124\n// stylelint-disable selector-no-qualifying-type\ntextarea {\n  &.form-control {\n    min-height: $input-height;\n  }\n\n  &.form-control-sm {\n    min-height: $input-height-sm;\n  }\n\n  &.form-control-lg {\n    min-height: $input-height-lg;\n  }\n}\n// stylelint-enable selector-no-qualifying-type\n\n.form-control-color {\n  width: $form-color-width;\n  height: $input-height;\n  padding: $input-padding-y;\n\n  &:not(:disabled):not([readonly]) {\n    cursor: pointer;\n  }\n\n  &::-moz-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &::-webkit-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &.form-control-sm { height: $input-height-sm; }\n  &.form-control-lg { height: $input-height-lg; }\n}\n","// stylelint-disable property-disallowed-list\n@mixin transition($transition...) {\n  @if length($transition) == 0 {\n    $transition: $transition-base;\n  }\n\n  @if length($transition) > 1 {\n    @each $value in $transition {\n      @if $value == null or $value == none {\n        @warn \"The keyword 'none' or 'null' must be used as a single argument.\";\n      }\n    }\n  }\n\n  @if $enable-transitions {\n    @if nth($transition, 1) != null {\n      transition: $transition;\n    }\n\n    @if $enable-reduced-motion and nth($transition, 1) != null and nth($transition, 1) != none {\n      @media (prefers-reduced-motion: reduce) {\n        transition: none;\n      }\n    }\n  }\n}\n","// Gradients\n\n// scss-docs-start gradient-bg-mixin\n@mixin gradient-bg($color: null) {\n  background-color: $color;\n\n  @if $enable-gradients {\n    background-image: var(--#{$prefix}gradient);\n  }\n}\n// scss-docs-end gradient-bg-mixin\n\n// scss-docs-start gradient-mixins\n// Horizontal gradient, from left to right\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-x($start-color: $gray-700, $end-color: $gray-800, $start-percent: 0%, $end-percent: 100%) {\n  background-image: linear-gradient(to right, $start-color $start-percent, $end-color $end-percent);\n}\n\n// Vertical gradient, from top to bottom\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-y($start-color: $gray-700, $end-color: $gray-800, $start-percent: null, $end-percent: null) {\n  background-image: linear-gradient(to bottom, $start-color $start-percent, $end-color $end-percent);\n}\n\n@mixin gradient-directional($start-color: $gray-700, $end-color: $gray-800, $deg: 45deg) {\n  background-image: linear-gradient($deg, $start-color, $end-color);\n}\n\n@mixin gradient-x-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient(to right, $start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-y-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient($start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-radial($inner-color: $gray-700, $outer-color: $gray-800) {\n  background-image: radial-gradient(circle, $inner-color, $outer-color);\n}\n\n@mixin gradient-striped($color: rgba($white, .15), $angle: 45deg) {\n  background-image: linear-gradient($angle, $color 25%, transparent 25%, transparent 50%, $color 50%, $color 75%, transparent 75%, transparent);\n}\n// scss-docs-end gradient-mixins\n","// Select\n//\n// Replaces the browser default select with a custom one, mostly pulled from\n// https://primer.github.io/.\n\n.form-select {\n  --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator)};\n\n  display: block;\n  width: 100%;\n  padding: $form-select-padding-y $form-select-indicator-padding $form-select-padding-y $form-select-padding-x;\n  font-family: $form-select-font-family;\n  @include font-size($form-select-font-size);\n  font-weight: $form-select-font-weight;\n  line-height: $form-select-line-height;\n  color: $form-select-color;\n  appearance: none;\n  background-color: $form-select-bg;\n  background-image: var(--#{$prefix}form-select-bg-img), var(--#{$prefix}form-select-bg-icon, none);\n  background-repeat: no-repeat;\n  background-position: $form-select-bg-position;\n  background-size: $form-select-bg-size;\n  border: $form-select-border-width solid $form-select-border-color;\n  @include border-radius($form-select-border-radius, 0);\n  @include box-shadow($form-select-box-shadow);\n  @include transition($form-select-transition);\n\n  &:focus {\n    border-color: $form-select-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($form-select-box-shadow, $form-select-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $form-select-focus-box-shadow;\n    }\n  }\n\n  &[multiple],\n  &[size]:not([size=\"1\"]) {\n    padding-right: $form-select-padding-x;\n    background-image: none;\n  }\n\n  &:disabled {\n    color: $form-select-disabled-color;\n    background-color: $form-select-disabled-bg;\n    border-color: $form-select-disabled-border-color;\n  }\n\n  // Remove outline from select box in FF\n  &:-moz-focusring {\n    color: transparent;\n    text-shadow: 0 0 0 $form-select-color;\n  }\n}\n\n.form-select-sm {\n  padding-top: $form-select-padding-y-sm;\n  padding-bottom: $form-select-padding-y-sm;\n  padding-left: $form-select-padding-x-sm;\n  @include font-size($form-select-font-size-sm);\n  @include border-radius($form-select-border-radius-sm);\n}\n\n.form-select-lg {\n  padding-top: $form-select-padding-y-lg;\n  padding-bottom: $form-select-padding-y-lg;\n  padding-left: $form-select-padding-x-lg;\n  @include font-size($form-select-font-size-lg);\n  @include border-radius($form-select-border-radius-lg);\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-select {\n      --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator-dark)};\n    }\n  }\n}\n","//\n// Check/radio\n//\n\n.form-check {\n  display: block;\n  min-height: $form-check-min-height;\n  padding-left: $form-check-padding-start;\n  margin-bottom: $form-check-margin-bottom;\n\n  .form-check-input {\n    float: left;\n    margin-left: $form-check-padding-start * -1;\n  }\n}\n\n.form-check-reverse {\n  padding-right: $form-check-padding-start;\n  padding-left: 0;\n  text-align: right;\n\n  .form-check-input {\n    float: right;\n    margin-right: $form-check-padding-start * -1;\n    margin-left: 0;\n  }\n}\n\n.form-check-input {\n  --#{$prefix}form-check-bg: #{$form-check-input-bg};\n\n  flex-shrink: 0;\n  width: $form-check-input-width;\n  height: $form-check-input-width;\n  margin-top: ($line-height-base - $form-check-input-width) * .5; // line-height minus check height\n  vertical-align: top;\n  appearance: none;\n  background-color: var(--#{$prefix}form-check-bg);\n  background-image: var(--#{$prefix}form-check-bg-image);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: contain;\n  border: $form-check-input-border;\n  print-color-adjust: exact; // Keep themed appearance for print\n  @include transition($form-check-transition);\n\n  &[type=\"checkbox\"] {\n    @include border-radius($form-check-input-border-radius);\n  }\n\n  &[type=\"radio\"] {\n    // stylelint-disable-next-line property-disallowed-list\n    border-radius: $form-check-radio-border-radius;\n  }\n\n  &:active {\n    filter: $form-check-input-active-filter;\n  }\n\n  &:focus {\n    border-color: $form-check-input-focus-border;\n    outline: 0;\n    box-shadow: $form-check-input-focus-box-shadow;\n  }\n\n  &:checked {\n    background-color: $form-check-input-checked-bg-color;\n    border-color: $form-check-input-checked-border-color;\n\n    &[type=\"checkbox\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)};\n      }\n    }\n\n    &[type=\"radio\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)};\n      }\n    }\n  }\n\n  &[type=\"checkbox\"]:indeterminate {\n    background-color: $form-check-input-indeterminate-bg-color;\n    border-color: $form-check-input-indeterminate-border-color;\n\n    @if $enable-gradients {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)}, var(--#{$prefix}gradient);\n    } @else {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)};\n    }\n  }\n\n  &:disabled {\n    pointer-events: none;\n    filter: none;\n    opacity: $form-check-input-disabled-opacity;\n  }\n\n  // Use disabled attribute in addition of :disabled pseudo-class\n  // See: https://github.com/twbs/bootstrap/issues/28247\n  &[disabled],\n  &:disabled {\n    ~ .form-check-label {\n      cursor: default;\n      opacity: $form-check-label-disabled-opacity;\n    }\n  }\n}\n\n.form-check-label {\n  color: $form-check-label-color;\n  cursor: $form-check-label-cursor;\n}\n\n//\n// Switch\n//\n\n.form-switch {\n  padding-left: $form-switch-padding-start;\n\n  .form-check-input {\n    --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image)};\n\n    width: $form-switch-width;\n    margin-left: $form-switch-padding-start * -1;\n    background-image: var(--#{$prefix}form-switch-bg);\n    background-position: left center;\n    @include border-radius($form-switch-border-radius);\n    @include transition($form-switch-transition);\n\n    &:focus {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-focus-bg-image)};\n    }\n\n    &:checked {\n      background-position: $form-switch-checked-bg-position;\n\n      @if $enable-gradients {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)};\n      }\n    }\n  }\n\n  &.form-check-reverse {\n    padding-right: $form-switch-padding-start;\n    padding-left: 0;\n\n    .form-check-input {\n      margin-right: $form-switch-padding-start * -1;\n      margin-left: 0;\n    }\n  }\n}\n\n.form-check-inline {\n  display: inline-block;\n  margin-right: $form-check-inline-margin-end;\n}\n\n.btn-check {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n\n  &[disabled],\n  &:disabled {\n    + .btn {\n      pointer-events: none;\n      filter: none;\n      opacity: $form-check-btn-check-disabled-opacity;\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-switch .form-check-input:not(:checked):not(:focus) {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image-dark)};\n    }\n  }\n}\n","// Range\n//\n// Style range inputs the same across browsers. Vendor-specific rules for pseudo\n// elements cannot be mixed. As such, there are no shared styles for focus or\n// active states on prefixed selectors.\n\n.form-range {\n  width: 100%;\n  height: add($form-range-thumb-height, $form-range-thumb-focus-box-shadow-width * 2);\n  padding: 0; // Need to reset padding\n  appearance: none;\n  background-color: transparent;\n\n  &:focus {\n    outline: 0;\n\n    // Pseudo-elements must be split across multiple rulesets to have an effect.\n    // No box-shadow() mixin for focus accessibility.\n    &::-webkit-slider-thumb { box-shadow: $form-range-thumb-focus-box-shadow; }\n    &::-moz-range-thumb     { box-shadow: $form-range-thumb-focus-box-shadow; }\n  }\n\n  &::-moz-focus-outer {\n    border: 0;\n  }\n\n  &::-webkit-slider-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    margin-top: ($form-range-track-height - $form-range-thumb-height) * .5; // Webkit specific\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-webkit-slider-runnable-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent; // Why?\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent;\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &::-moz-range-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-moz-range-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent;\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent; // Firefox specific?\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &:disabled {\n    pointer-events: none;\n\n    &::-webkit-slider-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n\n    &::-moz-range-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n  }\n}\n",".form-floating {\n  position: relative;\n\n  > .form-control,\n  > .form-control-plaintext,\n  > .form-select {\n    height: $form-floating-height;\n    min-height: $form-floating-height;\n    line-height: $form-floating-line-height;\n  }\n\n  > label {\n    position: absolute;\n    top: 0;\n    left: 0;\n    z-index: 2;\n    height: 100%; // allow textareas\n    padding: $form-floating-padding-y $form-floating-padding-x;\n    overflow: hidden;\n    text-align: start;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n    pointer-events: none;\n    border: $input-border-width solid transparent; // Required for aligning label's text with the input as it affects inner box model\n    transform-origin: 0 0;\n    @include transition($form-floating-transition);\n  }\n\n  > .form-control,\n  > .form-control-plaintext {\n    padding: $form-floating-padding-y $form-floating-padding-x;\n\n    &::placeholder {\n      color: transparent;\n    }\n\n    &:focus,\n    &:not(:placeholder-shown) {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n    // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n    &:-webkit-autofill {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n  }\n\n  > .form-select {\n    padding-top: $form-floating-input-padding-t;\n    padding-bottom: $form-floating-input-padding-b;\n  }\n\n  > .form-control:focus,\n  > .form-control:not(:placeholder-shown),\n  > .form-control-plaintext,\n  > .form-select {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n\n      &::after {\n        position: absolute;\n        inset: $form-floating-padding-y ($form-floating-padding-x * .5);\n        z-index: -1;\n        height: $form-floating-label-height;\n        content: \"\";\n        background-color: $input-bg;\n        @include border-radius($input-border-radius);\n      }\n    }\n  }\n  // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n  > .form-control:-webkit-autofill {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n    }\n  }\n\n  > .form-control-plaintext {\n    ~ label {\n      border-width: $input-border-width 0; // Required to properly position label text - as explained above\n    }\n  }\n\n  > :disabled ~ label,\n  > .form-control:disabled ~ label { // Required for `.form-control`s because of specificity\n    color: $form-floating-label-disabled-color;\n\n    &::after {\n      background-color: $input-disabled-bg;\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.input-group {\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // For form validation feedback\n  align-items: stretch;\n  width: 100%;\n\n  > .form-control,\n  > .form-select,\n  > .form-floating {\n    position: relative; // For focus state's z-index\n    flex: 1 1 auto;\n    width: 1%;\n    min-width: 0; // https://stackoverflow.com/questions/36247140/why-dont-flex-items-shrink-past-content-size\n  }\n\n  // Bring the \"active\" form control to the top of surrounding elements\n  > .form-control:focus,\n  > .form-select:focus,\n  > .form-floating:focus-within {\n    z-index: 5;\n  }\n\n  // Ensure buttons are always above inputs for more visually pleasing borders.\n  // This isn't needed for `.input-group-text` since it shares the same border-color\n  // as our inputs.\n  .btn {\n    position: relative;\n    z-index: 2;\n\n    &:focus {\n      z-index: 5;\n    }\n  }\n}\n\n\n// Textual addons\n//\n// Serves as a catch-all element for any text or radio/checkbox input you wish\n// to prepend or append to an input.\n\n.input-group-text {\n  display: flex;\n  align-items: center;\n  padding: $input-group-addon-padding-y $input-group-addon-padding-x;\n  @include font-size($input-font-size); // Match inputs\n  font-weight: $input-group-addon-font-weight;\n  line-height: $input-line-height;\n  color: $input-group-addon-color;\n  text-align: center;\n  white-space: nowrap;\n  background-color: $input-group-addon-bg;\n  border: $input-border-width solid $input-group-addon-border-color;\n  @include border-radius($input-border-radius);\n}\n\n\n// Sizing\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .form-select,\n.input-group-lg > .input-group-text,\n.input-group-lg > .btn {\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n}\n\n.input-group-sm > .form-control,\n.input-group-sm > .form-select,\n.input-group-sm > .input-group-text,\n.input-group-sm > .btn {\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n}\n\n.input-group-lg > .form-select,\n.input-group-sm > .form-select {\n  padding-right: $form-select-padding-x + $form-select-indicator-padding;\n}\n\n\n// Rounded corners\n//\n// These rulesets must come after the sizing ones to properly override sm and lg\n// border-radius values when extending. They're more specific than we'd like\n// with the `.input-group >` part, but without it, we cannot override the sizing.\n\n// stylelint-disable-next-line no-duplicate-selectors\n.input-group {\n  &:not(.has-validation) {\n    > :not(:last-child):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 3),\n    > .form-floating:not(:last-child) > .form-control,\n    > .form-floating:not(:last-child) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  &.has-validation {\n    > :nth-last-child(n + 3):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 4),\n    > .form-floating:nth-last-child(n + 3) > .form-control,\n    > .form-floating:nth-last-child(n + 3) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  $validation-messages: \"\";\n  @each $state in map-keys($form-validation-states) {\n    $validation-messages: $validation-messages + \":not(.\" + unquote($state) + \"-tooltip)\" + \":not(.\" + unquote($state) + \"-feedback)\";\n  }\n\n  > :not(:first-child):not(.dropdown-menu)#{$validation-messages} {\n    margin-left: calc(#{$input-border-width} * -1); // stylelint-disable-line function-disallowed-list\n    @include border-start-radius(0);\n  }\n\n  > .form-floating:not(:first-child) > .form-control,\n  > .form-floating:not(:first-child) > .form-select {\n    @include border-start-radius(0);\n  }\n}\n","// This mixin uses an `if()` technique to be compatible with Dart Sass\n// See https://github.com/sass/sass/issues/1873#issuecomment-152293725 for more details\n\n// scss-docs-start form-validation-mixins\n@mixin form-validation-state-selector($state) {\n  @if ($state == \"valid\" or $state == \"invalid\") {\n    .was-validated #{if(&, \"&\", \"\")}:#{$state},\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  } @else {\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  }\n}\n\n@mixin form-validation-state(\n  $state,\n  $color,\n  $icon,\n  $tooltip-color: color-contrast($color),\n  $tooltip-bg-color: rgba($color, $form-feedback-tooltip-opacity),\n  $focus-box-shadow: 0 0 $input-btn-focus-blur $input-focus-width rgba($color, $input-btn-focus-color-opacity),\n  $border-color: $color\n) {\n  .#{$state}-feedback {\n    display: none;\n    width: 100%;\n    margin-top: $form-feedback-margin-top;\n    @include font-size($form-feedback-font-size);\n    font-style: $form-feedback-font-style;\n    color: $color;\n  }\n\n  .#{$state}-tooltip {\n    position: absolute;\n    top: 100%;\n    z-index: 5;\n    display: none;\n    max-width: 100%; // Contain to parent when possible\n    padding: $form-feedback-tooltip-padding-y $form-feedback-tooltip-padding-x;\n    margin-top: .1rem;\n    @include font-size($form-feedback-tooltip-font-size);\n    line-height: $form-feedback-tooltip-line-height;\n    color: $tooltip-color;\n    background-color: $tooltip-bg-color;\n    @include border-radius($form-feedback-tooltip-border-radius);\n  }\n\n  @include form-validation-state-selector($state) {\n    ~ .#{$state}-feedback,\n    ~ .#{$state}-tooltip {\n      display: block;\n    }\n  }\n\n  .form-control {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-image: escape-svg($icon);\n        background-repeat: no-repeat;\n        background-position: right $input-height-inner-quarter center;\n        background-size: $input-height-inner-half $input-height-inner-half;\n      }\n\n      &:focus {\n        border-color: $border-color;\n        box-shadow: $focus-box-shadow;\n      }\n    }\n  }\n\n  // stylelint-disable-next-line selector-no-qualifying-type\n  textarea.form-control {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-position: top $input-height-inner-quarter right $input-height-inner-quarter;\n      }\n    }\n  }\n\n  .form-select {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        &:not([multiple]):not([size]),\n        &:not([multiple])[size=\"1\"] {\n          --#{$prefix}form-select-bg-icon: #{escape-svg($icon)};\n          padding-right: $form-select-feedback-icon-padding-end;\n          background-position: $form-select-bg-position, $form-select-feedback-icon-position;\n          background-size: $form-select-bg-size, $form-select-feedback-icon-size;\n        }\n      }\n\n      &:focus {\n        border-color: $border-color;\n        box-shadow: $focus-box-shadow;\n      }\n    }\n  }\n\n  .form-control-color {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        width: add($form-color-width, $input-height-inner);\n      }\n    }\n  }\n\n  .form-check-input {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      &:checked {\n        background-color: $color;\n      }\n\n      &:focus {\n        box-shadow: $focus-box-shadow;\n      }\n\n      ~ .form-check-label {\n        color: $color;\n      }\n    }\n  }\n  .form-check-inline .form-check-input {\n    ~ .#{$state}-feedback {\n      margin-left: .5em;\n    }\n  }\n\n  .input-group {\n    > .form-control:not(:focus),\n    > .form-select:not(:focus),\n    > .form-floating:not(:focus-within) {\n      @include form-validation-state-selector($state) {\n        @if $state == \"valid\" {\n          z-index: 3;\n        } @else if $state == \"invalid\" {\n          z-index: 4;\n        }\n      }\n    }\n  }\n}\n// scss-docs-end form-validation-mixins\n","//\n// Base styles\n//\n\n.btn {\n  // scss-docs-start btn-css-vars\n  --#{$prefix}btn-padding-x: #{$btn-padding-x};\n  --#{$prefix}btn-padding-y: #{$btn-padding-y};\n  --#{$prefix}btn-font-family: #{$btn-font-family};\n  @include rfs($btn-font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-font-weight: #{$btn-font-weight};\n  --#{$prefix}btn-line-height: #{$btn-line-height};\n  --#{$prefix}btn-color: #{$btn-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-width: #{$btn-border-width};\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-border-radius: #{$btn-border-radius};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-box-shadow: #{$btn-box-shadow};\n  --#{$prefix}btn-disabled-opacity: #{$btn-disabled-opacity};\n  --#{$prefix}btn-focus-box-shadow: 0 0 0 #{$btn-focus-width} rgba(var(--#{$prefix}btn-focus-shadow-rgb), .5);\n  // scss-docs-end btn-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}btn-padding-y) var(--#{$prefix}btn-padding-x);\n  font-family: var(--#{$prefix}btn-font-family);\n  @include font-size(var(--#{$prefix}btn-font-size));\n  font-weight: var(--#{$prefix}btn-font-weight);\n  line-height: var(--#{$prefix}btn-line-height);\n  color: var(--#{$prefix}btn-color);\n  text-align: center;\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: $btn-white-space;\n  vertical-align: middle;\n  cursor: if($enable-button-pointers, pointer, null);\n  user-select: none;\n  border: var(--#{$prefix}btn-border-width) solid var(--#{$prefix}btn-border-color);\n  @include border-radius(var(--#{$prefix}btn-border-radius));\n  @include gradient-bg(var(--#{$prefix}btn-bg));\n  @include box-shadow(var(--#{$prefix}btn-box-shadow));\n  @include transition($btn-transition);\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}btn-hover-bg);\n    border-color: var(--#{$prefix}btn-hover-border-color);\n  }\n\n  .btn-check + &:hover {\n    // override for the checkbox/radio buttons\n    color: var(--#{$prefix}btn-color);\n    background-color: var(--#{$prefix}btn-bg);\n    border-color: var(--#{$prefix}btn-border-color);\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-hover-color);\n    @include gradient-bg(var(--#{$prefix}btn-hover-bg));\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:focus-visible + & {\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:checked + &,\n  :not(.btn-check) + &:active,\n  &:first-child:active,\n  &.active,\n  &.show {\n    color: var(--#{$prefix}btn-active-color);\n    background-color: var(--#{$prefix}btn-active-bg);\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-active-border-color);\n    @include box-shadow(var(--#{$prefix}btn-active-shadow));\n\n    &:focus-visible {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      @if $enable-shadows {\n        box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n      } @else {\n        box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n      }\n    }\n  }\n\n  &:disabled,\n  &.disabled,\n  fieldset:disabled & {\n    color: var(--#{$prefix}btn-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}btn-disabled-bg);\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-disabled-border-color);\n    opacity: var(--#{$prefix}btn-disabled-opacity);\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Alternate buttons\n//\n\n// scss-docs-start btn-variant-loops\n@each $color, $value in $theme-colors {\n  .btn-#{$color} {\n    @if $color == \"light\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: shade-color($value, $btn-hover-bg-shade-amount),\n        $hover-border: shade-color($value, $btn-hover-border-shade-amount),\n        $active-background: shade-color($value, $btn-active-bg-shade-amount),\n        $active-border: shade-color($value, $btn-active-border-shade-amount)\n      );\n    } @else if $color == \"dark\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: tint-color($value, $btn-hover-bg-tint-amount),\n        $hover-border: tint-color($value, $btn-hover-border-tint-amount),\n        $active-background: tint-color($value, $btn-active-bg-tint-amount),\n        $active-border: tint-color($value, $btn-active-border-tint-amount)\n      );\n    } @else {\n      @include button-variant($value, $value);\n    }\n  }\n}\n\n@each $color, $value in $theme-colors {\n  .btn-outline-#{$color} {\n    @include button-outline-variant($value);\n  }\n}\n// scss-docs-end btn-variant-loops\n\n\n//\n// Link buttons\n//\n\n// Make a button look and behave like a link\n.btn-link {\n  --#{$prefix}btn-font-weight: #{$font-weight-normal};\n  --#{$prefix}btn-color: #{$btn-link-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-hover-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-active-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-active-border-color: transparent;\n  --#{$prefix}btn-disabled-color: #{$btn-link-disabled-color};\n  --#{$prefix}btn-disabled-border-color: transparent;\n  --#{$prefix}btn-box-shadow: 0 0 0 #000; // Can't use `none` as keyword negates all values when used with multiple shadows\n  --#{$prefix}btn-focus-shadow-rgb: #{$btn-link-focus-shadow-rgb};\n\n  text-decoration: $link-decoration;\n  @if $enable-gradients {\n    background-image: none;\n  }\n\n  &:hover,\n  &:focus-visible {\n    text-decoration: $link-hover-decoration;\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-color);\n  }\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n  }\n\n  // No need for an active state here\n}\n\n\n//\n// Button Sizes\n//\n\n.btn-lg {\n  @include button-size($btn-padding-y-lg, $btn-padding-x-lg, $btn-font-size-lg, $btn-border-radius-lg);\n}\n\n.btn-sm {\n  @include button-size($btn-padding-y-sm, $btn-padding-x-sm, $btn-font-size-sm, $btn-border-radius-sm);\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n// scss-docs-start btn-variant-mixin\n@mixin button-variant(\n  $background,\n  $border,\n  $color: color-contrast($background),\n  $hover-background: if($color == $color-contrast-light, shade-color($background, $btn-hover-bg-shade-amount), tint-color($background, $btn-hover-bg-tint-amount)),\n  $hover-border: if($color == $color-contrast-light, shade-color($border, $btn-hover-border-shade-amount), tint-color($border, $btn-hover-border-tint-amount)),\n  $hover-color: color-contrast($hover-background),\n  $active-background: if($color == $color-contrast-light, shade-color($background, $btn-active-bg-shade-amount), tint-color($background, $btn-active-bg-tint-amount)),\n  $active-border: if($color == $color-contrast-light, shade-color($border, $btn-active-border-shade-amount), tint-color($border, $btn-active-border-tint-amount)),\n  $active-color: color-contrast($active-background),\n  $disabled-background: $background,\n  $disabled-border: $border,\n  $disabled-color: color-contrast($disabled-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-bg: #{$background};\n  --#{$prefix}btn-border-color: #{$border};\n  --#{$prefix}btn-hover-color: #{$hover-color};\n  --#{$prefix}btn-hover-bg: #{$hover-background};\n  --#{$prefix}btn-hover-border-color: #{$hover-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb(mix($color, $border, 15%))};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$disabled-color};\n  --#{$prefix}btn-disabled-bg: #{$disabled-background};\n  --#{$prefix}btn-disabled-border-color: #{$disabled-border};\n}\n// scss-docs-end btn-variant-mixin\n\n// scss-docs-start btn-outline-variant-mixin\n@mixin button-outline-variant(\n  $color,\n  $color-hover: color-contrast($color),\n  $active-background: $color,\n  $active-border: $color,\n  $active-color: color-contrast($active-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-border-color: #{$color};\n  --#{$prefix}btn-hover-color: #{$color-hover};\n  --#{$prefix}btn-hover-bg: #{$active-background};\n  --#{$prefix}btn-hover-border-color: #{$active-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb($color)};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$color};\n  --#{$prefix}btn-disabled-bg: transparent;\n  --#{$prefix}btn-disabled-border-color: #{$color};\n  --#{$prefix}gradient: none;\n}\n// scss-docs-end btn-outline-variant-mixin\n\n// scss-docs-start btn-size-mixin\n@mixin button-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}btn-padding-y: #{$padding-y};\n  --#{$prefix}btn-padding-x: #{$padding-x};\n  @include rfs($font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-border-radius: #{$border-radius};\n}\n// scss-docs-end btn-size-mixin\n",".fade {\n  @include transition($transition-fade);\n\n  &:not(.show) {\n    opacity: 0;\n  }\n}\n\n// scss-docs-start collapse-classes\n.collapse {\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.collapsing {\n  height: 0;\n  overflow: hidden;\n  @include transition($transition-collapse);\n\n  &.collapse-horizontal {\n    width: 0;\n    height: auto;\n    @include transition($transition-collapse-width);\n  }\n}\n// scss-docs-end collapse-classes\n","// The dropdown wrapper (`<div>`)\n.dropup,\n.dropend,\n.dropdown,\n.dropstart,\n.dropup-center,\n.dropdown-center {\n  position: relative;\n}\n\n.dropdown-toggle {\n  white-space: nowrap;\n\n  // Generate the caret automatically\n  @include caret();\n}\n\n// The dropdown menu\n.dropdown-menu {\n  // scss-docs-start dropdown-css-vars\n  --#{$prefix}dropdown-zindex: #{$zindex-dropdown};\n  --#{$prefix}dropdown-min-width: #{$dropdown-min-width};\n  --#{$prefix}dropdown-padding-x: #{$dropdown-padding-x};\n  --#{$prefix}dropdown-padding-y: #{$dropdown-padding-y};\n  --#{$prefix}dropdown-spacer: #{$dropdown-spacer};\n  @include rfs($dropdown-font-size, --#{$prefix}dropdown-font-size);\n  --#{$prefix}dropdown-color: #{$dropdown-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-border-color};\n  --#{$prefix}dropdown-border-radius: #{$dropdown-border-radius};\n  --#{$prefix}dropdown-border-width: #{$dropdown-border-width};\n  --#{$prefix}dropdown-inner-border-radius: #{$dropdown-inner-border-radius};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-divider-bg};\n  --#{$prefix}dropdown-divider-margin-y: #{$dropdown-divider-margin-y};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-link-hover-color};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-link-disabled-color};\n  --#{$prefix}dropdown-item-padding-x: #{$dropdown-item-padding-x};\n  --#{$prefix}dropdown-item-padding-y: #{$dropdown-item-padding-y};\n  --#{$prefix}dropdown-header-color: #{$dropdown-header-color};\n  --#{$prefix}dropdown-header-padding-x: #{$dropdown-header-padding-x};\n  --#{$prefix}dropdown-header-padding-y: #{$dropdown-header-padding-y};\n  // scss-docs-end dropdown-css-vars\n\n  position: absolute;\n  z-index: var(--#{$prefix}dropdown-zindex);\n  display: none; // none by default, but block on \"open\" of the menu\n  min-width: var(--#{$prefix}dropdown-min-width);\n  padding: var(--#{$prefix}dropdown-padding-y) var(--#{$prefix}dropdown-padding-x);\n  margin: 0; // Override default margin of ul\n  @include font-size(var(--#{$prefix}dropdown-font-size));\n  color: var(--#{$prefix}dropdown-color);\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  list-style: none;\n  background-color: var(--#{$prefix}dropdown-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}dropdown-border-width) solid var(--#{$prefix}dropdown-border-color);\n  @include border-radius(var(--#{$prefix}dropdown-border-radius));\n  @include box-shadow(var(--#{$prefix}dropdown-box-shadow));\n\n  &[data-bs-popper] {\n    top: 100%;\n    left: 0;\n    margin-top: var(--#{$prefix}dropdown-spacer);\n  }\n\n  @if $dropdown-padding-y == 0 {\n    > .dropdown-item:first-child,\n    > li:first-child .dropdown-item {\n      @include border-top-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n    > .dropdown-item:last-child,\n    > li:last-child .dropdown-item {\n      @include border-bottom-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n\n  }\n}\n\n// scss-docs-start responsive-breakpoints\n// We deliberately hardcode the `bs-` prefix because we check\n// this custom property in JS to determine Popper's positioning\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .dropdown-menu#{$infix}-start {\n      --bs-position: start;\n\n      &[data-bs-popper] {\n        right: auto;\n        left: 0;\n      }\n    }\n\n    .dropdown-menu#{$infix}-end {\n      --bs-position: end;\n\n      &[data-bs-popper] {\n        right: 0;\n        left: auto;\n      }\n    }\n  }\n}\n// scss-docs-end responsive-breakpoints\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n// Just add .dropup after the standard .dropdown class and you're set.\n.dropup {\n  .dropdown-menu[data-bs-popper] {\n    top: auto;\n    bottom: 100%;\n    margin-top: 0;\n    margin-bottom: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(up);\n  }\n}\n\n.dropend {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: auto;\n    left: 100%;\n    margin-top: 0;\n    margin-left: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(end);\n    &::after {\n      vertical-align: 0;\n    }\n  }\n}\n\n.dropstart {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: 100%;\n    left: auto;\n    margin-top: 0;\n    margin-right: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(start);\n    &::before {\n      vertical-align: 0;\n    }\n  }\n}\n\n\n// Dividers (basically an `<hr>`) within the dropdown\n.dropdown-divider {\n  height: 0;\n  margin: var(--#{$prefix}dropdown-divider-margin-y) 0;\n  overflow: hidden;\n  border-top: 1px solid var(--#{$prefix}dropdown-divider-bg);\n  opacity: 1; // Revisit in v6 to de-dupe styles that conflict with <hr> element\n}\n\n// Links, buttons, and more within the dropdown menu\n//\n// `<button>`-specific styles are denoted with `// For <button>s`\n.dropdown-item {\n  display: block;\n  width: 100%; // For `<button>`s\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  clear: both;\n  font-weight: $font-weight-normal;\n  color: var(--#{$prefix}dropdown-link-color);\n  text-align: inherit; // For `<button>`s\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap; // prevent links from randomly breaking onto new lines\n  background-color: transparent; // For `<button>`s\n  border: 0; // For `<button>`s\n  @include border-radius(var(--#{$prefix}dropdown-item-border-radius, 0));\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}dropdown-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    @include gradient-bg(var(--#{$prefix}dropdown-link-hover-bg));\n  }\n\n  &.active,\n  &:active {\n    color: var(--#{$prefix}dropdown-link-active-color);\n    text-decoration: none;\n    @include gradient-bg(var(--#{$prefix}dropdown-link-active-bg));\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}dropdown-link-disabled-color);\n    pointer-events: none;\n    background-color: transparent;\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n  }\n}\n\n.dropdown-menu.show {\n  display: block;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: var(--#{$prefix}dropdown-header-padding-y) var(--#{$prefix}dropdown-header-padding-x);\n  margin-bottom: 0; // for use with heading elements\n  @include font-size($font-size-sm);\n  color: var(--#{$prefix}dropdown-header-color);\n  white-space: nowrap; // as with > li > a\n}\n\n// Dropdown text\n.dropdown-item-text {\n  display: block;\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  color: var(--#{$prefix}dropdown-link-color);\n}\n\n// Dark dropdowns\n.dropdown-menu-dark {\n  // scss-docs-start dropdown-dark-css-vars\n  --#{$prefix}dropdown-color: #{$dropdown-dark-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-dark-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-dark-border-color};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-dark-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-dark-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-dark-link-hover-color};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-dark-divider-bg};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-dark-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-dark-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-dark-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-dark-link-disabled-color};\n  --#{$prefix}dropdown-header-color: #{$dropdown-dark-header-color};\n  // scss-docs-end dropdown-dark-css-vars\n}\n","// scss-docs-start caret-mixins\n@mixin caret-down($width: $caret-width) {\n  border-top: $width solid;\n  border-right: $width solid transparent;\n  border-bottom: 0;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-up($width: $caret-width) {\n  border-top: 0;\n  border-right: $width solid transparent;\n  border-bottom: $width solid;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-end($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: 0;\n  border-bottom: $width solid transparent;\n  border-left: $width solid;\n}\n\n@mixin caret-start($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: $width solid;\n  border-bottom: $width solid transparent;\n}\n\n@mixin caret(\n  $direction: down,\n  $width: $caret-width,\n  $spacing: $caret-spacing,\n  $vertical-align: $caret-vertical-align\n) {\n  @if $enable-caret {\n    &::after {\n      display: inline-block;\n      margin-left: $spacing;\n      vertical-align: $vertical-align;\n      content: \"\";\n      @if $direction == down {\n        @include caret-down($width);\n      } @else if $direction == up {\n        @include caret-up($width);\n      } @else if $direction == end {\n        @include caret-end($width);\n      }\n    }\n\n    @if $direction == start {\n      &::after {\n        display: none;\n      }\n\n      &::before {\n        display: inline-block;\n        margin-right: $spacing;\n        vertical-align: $vertical-align;\n        content: \"\";\n        @include caret-start($width);\n      }\n    }\n\n    &:empty::after {\n      margin-left: 0;\n    }\n  }\n}\n// scss-docs-end caret-mixins\n","// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-flex;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n\n  > .btn {\n    position: relative;\n    flex: 1 1 auto;\n  }\n\n  // Bring the hover, focused, and \"active\" buttons to the front to overlay\n  // the borders properly\n  > .btn-check:checked + .btn,\n  > .btn-check:focus + .btn,\n  > .btn:hover,\n  > .btn:focus,\n  > .btn:active,\n  > .btn.active {\n    z-index: 1;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  display: flex;\n  flex-wrap: wrap;\n  justify-content: flex-start;\n\n  .input-group {\n    width: auto;\n  }\n}\n\n.btn-group {\n  @include border-radius($btn-border-radius);\n\n  // Prevent double borders when buttons are next to each other\n  > :not(.btn-check:first-child) + .btn,\n  > .btn-group:not(:first-child) {\n    margin-left: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn.dropdown-toggle-split:first-child,\n  > .btn-group:not(:last-child) > .btn {\n    @include border-end-radius(0);\n  }\n\n  // The left radius should be 0 if the button is:\n  // - the \"third or more\" child\n  // - the second child and the previous element isn't `.btn-check` (making it the first child visually)\n  // - part of a btn-group which isn't the first child\n  > .btn:nth-child(n + 3),\n  > :not(.btn-check) + .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-start-radius(0);\n  }\n}\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-sm > .btn { @extend .btn-sm; }\n.btn-group-lg > .btn { @extend .btn-lg; }\n\n\n//\n// Split button dropdowns\n//\n\n.dropdown-toggle-split {\n  padding-right: $btn-padding-x * .75;\n  padding-left: $btn-padding-x * .75;\n\n  &::after,\n  .dropup &::after,\n  .dropend &::after {\n    margin-left: 0;\n  }\n\n  .dropstart &::before {\n    margin-right: 0;\n  }\n}\n\n.btn-sm + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-sm * .75;\n  padding-left: $btn-padding-x-sm * .75;\n}\n\n.btn-lg + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-lg * .75;\n  padding-left: $btn-padding-x-lg * .75;\n}\n\n\n// The clickable button for toggling the menu\n// Set the same inset shadow as the :active state\n.btn-group.show .dropdown-toggle {\n  @include box-shadow($btn-active-box-shadow);\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Vertical button groups\n//\n\n.btn-group-vertical {\n  flex-direction: column;\n  align-items: flex-start;\n  justify-content: center;\n\n  > .btn,\n  > .btn-group {\n    width: 100%;\n  }\n\n  > .btn:not(:first-child),\n  > .btn-group:not(:first-child) {\n    margin-top: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn-group:not(:last-child) > .btn {\n    @include border-bottom-radius(0);\n  }\n\n  > .btn ~ .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-top-radius(0);\n  }\n}\n","// Base class\n//\n// Kickstart any navigation component with a set of style resets. Works with\n// `<nav>`s, `<ul>`s or `<ol>`s.\n\n.nav {\n  // scss-docs-start nav-css-vars\n  --#{$prefix}nav-link-padding-x: #{$nav-link-padding-x};\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: #{$nav-link-color};\n  --#{$prefix}nav-link-hover-color: #{$nav-link-hover-color};\n  --#{$prefix}nav-link-disabled-color: #{$nav-link-disabled-color};\n  // scss-docs-end nav-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n\n.nav-link {\n  display: block;\n  padding: var(--#{$prefix}nav-link-padding-y) var(--#{$prefix}nav-link-padding-x);\n  @include font-size(var(--#{$prefix}nav-link-font-size));\n  font-weight: var(--#{$prefix}nav-link-font-weight);\n  color: var(--#{$prefix}nav-link-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background: none;\n  border: 0;\n  @include transition($nav-link-transition);\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}nav-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  &:focus-visible {\n    outline: 0;\n    box-shadow: $nav-link-focus-box-shadow;\n  }\n\n  // Disabled state lightens text\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}nav-link-disabled-color);\n    pointer-events: none;\n    cursor: default;\n  }\n}\n\n//\n// Tabs\n//\n\n.nav-tabs {\n  // scss-docs-start nav-tabs-css-vars\n  --#{$prefix}nav-tabs-border-width: #{$nav-tabs-border-width};\n  --#{$prefix}nav-tabs-border-color: #{$nav-tabs-border-color};\n  --#{$prefix}nav-tabs-border-radius: #{$nav-tabs-border-radius};\n  --#{$prefix}nav-tabs-link-hover-border-color: #{$nav-tabs-link-hover-border-color};\n  --#{$prefix}nav-tabs-link-active-color: #{$nav-tabs-link-active-color};\n  --#{$prefix}nav-tabs-link-active-bg: #{$nav-tabs-link-active-bg};\n  --#{$prefix}nav-tabs-link-active-border-color: #{$nav-tabs-link-active-border-color};\n  // scss-docs-end nav-tabs-css-vars\n\n  border-bottom: var(--#{$prefix}nav-tabs-border-width) solid var(--#{$prefix}nav-tabs-border-color);\n\n  .nav-link {\n    margin-bottom: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    border: var(--#{$prefix}nav-tabs-border-width) solid transparent;\n    @include border-top-radius(var(--#{$prefix}nav-tabs-border-radius));\n\n    &:hover,\n    &:focus {\n      // Prevents active .nav-link tab overlapping focus outline of previous/next .nav-link\n      isolation: isolate;\n      border-color: var(--#{$prefix}nav-tabs-link-hover-border-color);\n    }\n  }\n\n  .nav-link.active,\n  .nav-item.show .nav-link {\n    color: var(--#{$prefix}nav-tabs-link-active-color);\n    background-color: var(--#{$prefix}nav-tabs-link-active-bg);\n    border-color: var(--#{$prefix}nav-tabs-link-active-border-color);\n  }\n\n  .dropdown-menu {\n    // Make dropdown border overlap tab border\n    margin-top: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    // Remove the top rounded corners here since there is a hard edge above the menu\n    @include border-top-radius(0);\n  }\n}\n\n\n//\n// Pills\n//\n\n.nav-pills {\n  // scss-docs-start nav-pills-css-vars\n  --#{$prefix}nav-pills-border-radius: #{$nav-pills-border-radius};\n  --#{$prefix}nav-pills-link-active-color: #{$nav-pills-link-active-color};\n  --#{$prefix}nav-pills-link-active-bg: #{$nav-pills-link-active-bg};\n  // scss-docs-end nav-pills-css-vars\n\n  .nav-link {\n    @include border-radius(var(--#{$prefix}nav-pills-border-radius));\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    color: var(--#{$prefix}nav-pills-link-active-color);\n    @include gradient-bg(var(--#{$prefix}nav-pills-link-active-bg));\n  }\n}\n\n\n//\n// Underline\n//\n\n.nav-underline {\n  // scss-docs-start nav-underline-css-vars\n  --#{$prefix}nav-underline-gap: #{$nav-underline-gap};\n  --#{$prefix}nav-underline-border-width: #{$nav-underline-border-width};\n  --#{$prefix}nav-underline-link-active-color: #{$nav-underline-link-active-color};\n  // scss-docs-end nav-underline-css-vars\n\n  gap: var(--#{$prefix}nav-underline-gap);\n\n  .nav-link {\n    padding-right: 0;\n    padding-left: 0;\n    border-bottom: var(--#{$prefix}nav-underline-border-width) solid transparent;\n\n    &:hover,\n    &:focus {\n      border-bottom-color: currentcolor;\n    }\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    font-weight: $font-weight-bold;\n    color: var(--#{$prefix}nav-underline-link-active-color);\n    border-bottom-color: currentcolor;\n  }\n}\n\n\n//\n// Justified variants\n//\n\n.nav-fill {\n  > .nav-link,\n  .nav-item {\n    flex: 1 1 auto;\n    text-align: center;\n  }\n}\n\n.nav-justified {\n  > .nav-link,\n  .nav-item {\n    flex-basis: 0;\n    flex-grow: 1;\n    text-align: center;\n  }\n}\n\n.nav-fill,\n.nav-justified {\n  .nav-item .nav-link {\n    width: 100%; // Make sure button will grow\n  }\n}\n\n\n// Tabbable tabs\n//\n// Hide tabbable panes to start, show them when `.active`\n\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n","// Navbar\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  // scss-docs-start navbar-css-vars\n  --#{$prefix}navbar-padding-x: #{if($navbar-padding-x == null, 0, $navbar-padding-x)};\n  --#{$prefix}navbar-padding-y: #{$navbar-padding-y};\n  --#{$prefix}navbar-color: #{$navbar-light-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-light-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-light-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-light-active-color};\n  --#{$prefix}navbar-brand-padding-y: #{$navbar-brand-padding-y};\n  --#{$prefix}navbar-brand-margin-end: #{$navbar-brand-margin-end};\n  --#{$prefix}navbar-brand-font-size: #{$navbar-brand-font-size};\n  --#{$prefix}navbar-brand-color: #{$navbar-light-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-light-brand-hover-color};\n  --#{$prefix}navbar-nav-link-padding-x: #{$navbar-nav-link-padding-x};\n  --#{$prefix}navbar-toggler-padding-y: #{$navbar-toggler-padding-y};\n  --#{$prefix}navbar-toggler-padding-x: #{$navbar-toggler-padding-x};\n  --#{$prefix}navbar-toggler-font-size: #{$navbar-toggler-font-size};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-light-toggler-icon-bg)};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-light-toggler-border-color};\n  --#{$prefix}navbar-toggler-border-radius: #{$navbar-toggler-border-radius};\n  --#{$prefix}navbar-toggler-focus-width: #{$navbar-toggler-focus-width};\n  --#{$prefix}navbar-toggler-transition: #{$navbar-toggler-transition};\n  // scss-docs-end navbar-css-vars\n\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // allow us to do the line break for collapsing content\n  align-items: center;\n  justify-content: space-between; // space out brand from logo\n  padding: var(--#{$prefix}navbar-padding-y) var(--#{$prefix}navbar-padding-x);\n  @include gradient-bg();\n\n  // Because flex properties aren't inherited, we need to redeclare these first\n  // few properties so that content nested within behave properly.\n  // The `flex-wrap` property is inherited to simplify the expanded navbars\n  %container-flex-properties {\n    display: flex;\n    flex-wrap: inherit;\n    align-items: center;\n    justify-content: space-between;\n  }\n\n  > .container,\n  > .container-fluid {\n    @extend %container-flex-properties;\n  }\n\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    > .container#{breakpoint-infix($breakpoint, $container-max-widths)} {\n      @extend %container-flex-properties;\n    }\n  }\n}\n\n\n// Navbar brand\n//\n// Used for brand, project, or site names.\n\n.navbar-brand {\n  padding-top: var(--#{$prefix}navbar-brand-padding-y);\n  padding-bottom: var(--#{$prefix}navbar-brand-padding-y);\n  margin-right: var(--#{$prefix}navbar-brand-margin-end);\n  @include font-size(var(--#{$prefix}navbar-brand-font-size));\n  color: var(--#{$prefix}navbar-brand-color);\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap;\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}navbar-brand-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n}\n\n\n// Navbar nav\n//\n// Custom navbar navigation (doesn't require `.nav`, but does make use of `.nav-link`).\n\n.navbar-nav {\n  // scss-docs-start navbar-nav-css-vars\n  --#{$prefix}nav-link-padding-x: 0;\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: var(--#{$prefix}navbar-color);\n  --#{$prefix}nav-link-hover-color: var(--#{$prefix}navbar-hover-color);\n  --#{$prefix}nav-link-disabled-color: var(--#{$prefix}navbar-disabled-color);\n  // scss-docs-end navbar-nav-css-vars\n\n  display: flex;\n  flex-direction: column; // cannot use `inherit` to get the `.navbar`s value\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n\n  .nav-link {\n    &.active,\n    &.show {\n      color: var(--#{$prefix}navbar-active-color);\n    }\n  }\n\n  .dropdown-menu {\n    position: static;\n  }\n}\n\n\n// Navbar text\n//\n//\n\n.navbar-text {\n  padding-top: $nav-link-padding-y;\n  padding-bottom: $nav-link-padding-y;\n  color: var(--#{$prefix}navbar-color);\n\n  a,\n  a:hover,\n  a:focus  {\n    color: var(--#{$prefix}navbar-active-color);\n  }\n}\n\n\n// Responsive navbar\n//\n// Custom styles for responsive collapsing and toggling of navbar contents.\n// Powered by the collapse Bootstrap JavaScript plugin.\n\n// When collapsed, prevent the toggleable navbar contents from appearing in\n// the default flexbox row orientation. Requires the use of `flex-wrap: wrap`\n// on the `.navbar` parent.\n.navbar-collapse {\n  flex-basis: 100%;\n  flex-grow: 1;\n  // For always expanded or extra full navbars, ensure content aligns itself\n  // properly vertically. Can be easily overridden with flex utilities.\n  align-items: center;\n}\n\n// Button for toggling the navbar when in its collapsed state\n.navbar-toggler {\n  padding: var(--#{$prefix}navbar-toggler-padding-y) var(--#{$prefix}navbar-toggler-padding-x);\n  @include font-size(var(--#{$prefix}navbar-toggler-font-size));\n  line-height: 1;\n  color: var(--#{$prefix}navbar-color);\n  background-color: transparent; // remove default button style\n  border: var(--#{$prefix}border-width) solid var(--#{$prefix}navbar-toggler-border-color); // remove default button style\n  @include border-radius(var(--#{$prefix}navbar-toggler-border-radius));\n  @include transition(var(--#{$prefix}navbar-toggler-transition));\n\n  &:hover {\n    text-decoration: none;\n  }\n\n  &:focus {\n    text-decoration: none;\n    outline: 0;\n    box-shadow: 0 0 0 var(--#{$prefix}navbar-toggler-focus-width);\n  }\n}\n\n// Keep as a separate element so folks can easily override it with another icon\n// or image file as needed.\n.navbar-toggler-icon {\n  display: inline-block;\n  width: 1.5em;\n  height: 1.5em;\n  vertical-align: middle;\n  background-image: var(--#{$prefix}navbar-toggler-icon-bg);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: 100%;\n}\n\n.navbar-nav-scroll {\n  max-height: var(--#{$prefix}scroll-height, 75vh);\n  overflow-y: auto;\n}\n\n// scss-docs-start navbar-expand-loop\n// Generate series of `.navbar-expand-*` responsive classes for configuring\n// where your navbar collapses.\n.navbar-expand {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $next: breakpoint-next($breakpoint, $grid-breakpoints);\n    $infix: breakpoint-infix($next, $grid-breakpoints);\n\n    // stylelint-disable-next-line scss/selector-no-union-class-name\n    &#{$infix} {\n      @include media-breakpoint-up($next) {\n        flex-wrap: nowrap;\n        justify-content: flex-start;\n\n        .navbar-nav {\n          flex-direction: row;\n\n          .dropdown-menu {\n            position: absolute;\n          }\n\n          .nav-link {\n            padding-right: var(--#{$prefix}navbar-nav-link-padding-x);\n            padding-left: var(--#{$prefix}navbar-nav-link-padding-x);\n          }\n        }\n\n        .navbar-nav-scroll {\n          overflow: visible;\n        }\n\n        .navbar-collapse {\n          display: flex !important; // stylelint-disable-line declaration-no-important\n          flex-basis: auto;\n        }\n\n        .navbar-toggler {\n          display: none;\n        }\n\n        .offcanvas {\n          // stylelint-disable declaration-no-important\n          position: static;\n          z-index: auto;\n          flex-grow: 1;\n          width: auto !important;\n          height: auto !important;\n          visibility: visible !important;\n          background-color: transparent !important;\n          border: 0 !important;\n          transform: none !important;\n          @include box-shadow(none);\n          @include transition(none);\n          // stylelint-enable declaration-no-important\n\n          .offcanvas-header {\n            display: none;\n          }\n\n          .offcanvas-body {\n            display: flex;\n            flex-grow: 0;\n            padding: 0;\n            overflow-y: visible;\n          }\n        }\n      }\n    }\n  }\n}\n// scss-docs-end navbar-expand-loop\n\n// Navbar themes\n//\n// Styles for switching between navbars with light or dark background.\n\n.navbar-light {\n  @include deprecate(\"`.navbar-light`\", \"v5.2.0\", \"v6.0.0\", true);\n}\n\n.navbar-dark,\n.navbar[data-bs-theme=\"dark\"] {\n  // scss-docs-start navbar-dark-css-vars\n  --#{$prefix}navbar-color: #{$navbar-dark-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-dark-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-dark-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-dark-active-color};\n  --#{$prefix}navbar-brand-color: #{$navbar-dark-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-dark-brand-hover-color};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-dark-toggler-border-color};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n  // scss-docs-end navbar-dark-css-vars\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .navbar-toggler-icon {\n      --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.card {\n  // scss-docs-start card-css-vars\n  --#{$prefix}card-spacer-y: #{$card-spacer-y};\n  --#{$prefix}card-spacer-x: #{$card-spacer-x};\n  --#{$prefix}card-title-spacer-y: #{$card-title-spacer-y};\n  --#{$prefix}card-title-color: #{$card-title-color};\n  --#{$prefix}card-subtitle-color: #{$card-subtitle-color};\n  --#{$prefix}card-border-width: #{$card-border-width};\n  --#{$prefix}card-border-color: #{$card-border-color};\n  --#{$prefix}card-border-radius: #{$card-border-radius};\n  --#{$prefix}card-box-shadow: #{$card-box-shadow};\n  --#{$prefix}card-inner-border-radius: #{$card-inner-border-radius};\n  --#{$prefix}card-cap-padding-y: #{$card-cap-padding-y};\n  --#{$prefix}card-cap-padding-x: #{$card-cap-padding-x};\n  --#{$prefix}card-cap-bg: #{$card-cap-bg};\n  --#{$prefix}card-cap-color: #{$card-cap-color};\n  --#{$prefix}card-height: #{$card-height};\n  --#{$prefix}card-color: #{$card-color};\n  --#{$prefix}card-bg: #{$card-bg};\n  --#{$prefix}card-img-overlay-padding: #{$card-img-overlay-padding};\n  --#{$prefix}card-group-margin: #{$card-group-margin};\n  // scss-docs-end card-css-vars\n\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  min-width: 0; // See https://github.com/twbs/bootstrap/pull/22740#issuecomment-305868106\n  height: var(--#{$prefix}card-height);\n  color: var(--#{$prefix}body-color);\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}card-bg);\n  background-clip: border-box;\n  border: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n  @include border-radius(var(--#{$prefix}card-border-radius));\n  @include box-shadow(var(--#{$prefix}card-box-shadow));\n\n  > hr {\n    margin-right: 0;\n    margin-left: 0;\n  }\n\n  > .list-group {\n    border-top: inherit;\n    border-bottom: inherit;\n\n    &:first-child {\n      border-top-width: 0;\n      @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n\n    &:last-child  {\n      border-bottom-width: 0;\n      @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n  }\n\n  // Due to specificity of the above selector (`.card > .list-group`), we must\n  // use a child selector here to prevent double borders.\n  > .card-header + .list-group,\n  > .list-group + .card-footer {\n    border-top: 0;\n  }\n}\n\n.card-body {\n  // Enable `flex-grow: 1` for decks and groups so that card blocks take up\n  // as much space as possible, ensuring footers are aligned to the bottom.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}card-spacer-y) var(--#{$prefix}card-spacer-x);\n  color: var(--#{$prefix}card-color);\n}\n\n.card-title {\n  margin-bottom: var(--#{$prefix}card-title-spacer-y);\n  color: var(--#{$prefix}card-title-color);\n}\n\n.card-subtitle {\n  margin-top: calc(-.5 * var(--#{$prefix}card-title-spacer-y)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: 0;\n  color: var(--#{$prefix}card-subtitle-color);\n}\n\n.card-text:last-child {\n  margin-bottom: 0;\n}\n\n.card-link {\n  &:hover {\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  + .card-link {\n    margin-left: var(--#{$prefix}card-spacer-x);\n  }\n}\n\n//\n// Optional textual caps\n//\n\n.card-header {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  margin-bottom: 0; // Removes the default margin-bottom of <hN>\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-bottom: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:first-child {\n    @include border-radius(var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius) 0 0);\n  }\n}\n\n.card-footer {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-top: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:last-child {\n    @include border-radius(0 0 var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius));\n  }\n}\n\n\n//\n// Header navs\n//\n\n.card-header-tabs {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: calc(-1 * var(--#{$prefix}card-cap-padding-y)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  border-bottom: 0;\n\n  .nav-link.active {\n    background-color: var(--#{$prefix}card-bg);\n    border-bottom-color: var(--#{$prefix}card-bg);\n  }\n}\n\n.card-header-pills {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n}\n\n// Card image\n.card-img-overlay {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  padding: var(--#{$prefix}card-img-overlay-padding);\n  @include border-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-top,\n.card-img-bottom {\n  width: 100%; // Required because we use flexbox and this inherently applies align-self: stretch\n}\n\n.card-img,\n.card-img-top {\n  @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-bottom {\n  @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n\n//\n// Card groups\n//\n\n.card-group {\n  // The child selector allows nested `.card` within `.card-group`\n  // to display properly.\n  > .card {\n    margin-bottom: var(--#{$prefix}card-group-margin);\n  }\n\n  @include media-breakpoint-up(sm) {\n    display: flex;\n    flex-flow: row wrap;\n    // The child selector allows nested `.card` within `.card-group`\n    // to display properly.\n    > .card {\n      // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      flex: 1 0 0%;\n      margin-bottom: 0;\n\n      + .card {\n        margin-left: 0;\n        border-left: 0;\n      }\n\n      // Handle rounded corners\n      @if $enable-rounded {\n        &:not(:last-child) {\n          @include border-end-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-right-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-right-radius: 0;\n          }\n        }\n\n        &:not(:first-child) {\n          @include border-start-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-left-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-left-radius: 0;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.accordion {\n  // scss-docs-start accordion-css-vars\n  --#{$prefix}accordion-color: #{$accordion-color};\n  --#{$prefix}accordion-bg: #{$accordion-bg};\n  --#{$prefix}accordion-transition: #{$accordion-transition};\n  --#{$prefix}accordion-border-color: #{$accordion-border-color};\n  --#{$prefix}accordion-border-width: #{$accordion-border-width};\n  --#{$prefix}accordion-border-radius: #{$accordion-border-radius};\n  --#{$prefix}accordion-inner-border-radius: #{$accordion-inner-border-radius};\n  --#{$prefix}accordion-btn-padding-x: #{$accordion-button-padding-x};\n  --#{$prefix}accordion-btn-padding-y: #{$accordion-button-padding-y};\n  --#{$prefix}accordion-btn-color: #{$accordion-button-color};\n  --#{$prefix}accordion-btn-bg: #{$accordion-button-bg};\n  --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon)};\n  --#{$prefix}accordion-btn-icon-width: #{$accordion-icon-width};\n  --#{$prefix}accordion-btn-icon-transform: #{$accordion-icon-transform};\n  --#{$prefix}accordion-btn-icon-transition: #{$accordion-icon-transition};\n  --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon)};\n  --#{$prefix}accordion-btn-focus-border-color: #{$accordion-button-focus-border-color};\n  --#{$prefix}accordion-btn-focus-box-shadow: #{$accordion-button-focus-box-shadow};\n  --#{$prefix}accordion-body-padding-x: #{$accordion-body-padding-x};\n  --#{$prefix}accordion-body-padding-y: #{$accordion-body-padding-y};\n  --#{$prefix}accordion-active-color: #{$accordion-button-active-color};\n  --#{$prefix}accordion-active-bg: #{$accordion-button-active-bg};\n  // scss-docs-end accordion-css-vars\n}\n\n.accordion-button {\n  position: relative;\n  display: flex;\n  align-items: center;\n  width: 100%;\n  padding: var(--#{$prefix}accordion-btn-padding-y) var(--#{$prefix}accordion-btn-padding-x);\n  @include font-size($font-size-base);\n  color: var(--#{$prefix}accordion-btn-color);\n  text-align: left; // Reset button style\n  background-color: var(--#{$prefix}accordion-btn-bg);\n  border: 0;\n  @include border-radius(0);\n  overflow-anchor: none;\n  @include transition(var(--#{$prefix}accordion-transition));\n\n  &:not(.collapsed) {\n    color: var(--#{$prefix}accordion-active-color);\n    background-color: var(--#{$prefix}accordion-active-bg);\n    box-shadow: inset 0 calc(-1 * var(--#{$prefix}accordion-border-width)) 0 var(--#{$prefix}accordion-border-color); // stylelint-disable-line function-disallowed-list\n\n    &::after {\n      background-image: var(--#{$prefix}accordion-btn-active-icon);\n      transform: var(--#{$prefix}accordion-btn-icon-transform);\n    }\n  }\n\n  // Accordion icon\n  &::after {\n    flex-shrink: 0;\n    width: var(--#{$prefix}accordion-btn-icon-width);\n    height: var(--#{$prefix}accordion-btn-icon-width);\n    margin-left: auto;\n    content: \"\";\n    background-image: var(--#{$prefix}accordion-btn-icon);\n    background-repeat: no-repeat;\n    background-size: var(--#{$prefix}accordion-btn-icon-width);\n    @include transition(var(--#{$prefix}accordion-btn-icon-transition));\n  }\n\n  &:hover {\n    z-index: 2;\n  }\n\n  &:focus {\n    z-index: 3;\n    border-color: var(--#{$prefix}accordion-btn-focus-border-color);\n    outline: 0;\n    box-shadow: var(--#{$prefix}accordion-btn-focus-box-shadow);\n  }\n}\n\n.accordion-header {\n  margin-bottom: 0;\n}\n\n.accordion-item {\n  color: var(--#{$prefix}accordion-color);\n  background-color: var(--#{$prefix}accordion-bg);\n  border: var(--#{$prefix}accordion-border-width) solid var(--#{$prefix}accordion-border-color);\n\n  &:first-of-type {\n    @include border-top-radius(var(--#{$prefix}accordion-border-radius));\n\n    .accordion-button {\n      @include border-top-radius(var(--#{$prefix}accordion-inner-border-radius));\n    }\n  }\n\n  &:not(:first-of-type) {\n    border-top: 0;\n  }\n\n  // Only set a border-radius on the last item if the accordion is collapsed\n  &:last-of-type {\n    @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n\n    .accordion-button {\n      &.collapsed {\n        @include border-bottom-radius(var(--#{$prefix}accordion-inner-border-radius));\n      }\n    }\n\n    .accordion-collapse {\n      @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n    }\n  }\n}\n\n.accordion-body {\n  padding: var(--#{$prefix}accordion-body-padding-y) var(--#{$prefix}accordion-body-padding-x);\n}\n\n\n// Flush accordion items\n//\n// Remove borders and border-radius to keep accordion items edge-to-edge.\n\n.accordion-flush {\n  .accordion-collapse {\n    border-width: 0;\n  }\n\n  .accordion-item {\n    border-right: 0;\n    border-left: 0;\n    @include border-radius(0);\n\n    &:first-child { border-top: 0; }\n    &:last-child { border-bottom: 0; }\n\n    .accordion-button {\n      &,\n      &.collapsed {\n        @include border-radius(0);\n      }\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .accordion-button::after {\n      --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon-dark)};\n      --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon-dark)};\n    }\n  }\n}\n",".breadcrumb {\n  // scss-docs-start breadcrumb-css-vars\n  --#{$prefix}breadcrumb-padding-x: #{$breadcrumb-padding-x};\n  --#{$prefix}breadcrumb-padding-y: #{$breadcrumb-padding-y};\n  --#{$prefix}breadcrumb-margin-bottom: #{$breadcrumb-margin-bottom};\n  @include rfs($breadcrumb-font-size, --#{$prefix}breadcrumb-font-size);\n  --#{$prefix}breadcrumb-bg: #{$breadcrumb-bg};\n  --#{$prefix}breadcrumb-border-radius: #{$breadcrumb-border-radius};\n  --#{$prefix}breadcrumb-divider-color: #{$breadcrumb-divider-color};\n  --#{$prefix}breadcrumb-item-padding-x: #{$breadcrumb-item-padding-x};\n  --#{$prefix}breadcrumb-item-active-color: #{$breadcrumb-active-color};\n  // scss-docs-end breadcrumb-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding: var(--#{$prefix}breadcrumb-padding-y) var(--#{$prefix}breadcrumb-padding-x);\n  margin-bottom: var(--#{$prefix}breadcrumb-margin-bottom);\n  @include font-size(var(--#{$prefix}breadcrumb-font-size));\n  list-style: none;\n  background-color: var(--#{$prefix}breadcrumb-bg);\n  @include border-radius(var(--#{$prefix}breadcrumb-border-radius));\n}\n\n.breadcrumb-item {\n  // The separator between breadcrumbs (by default, a forward-slash: \"/\")\n  + .breadcrumb-item {\n    padding-left: var(--#{$prefix}breadcrumb-item-padding-x);\n\n    &::before {\n      float: left; // Suppress inline spacings and underlining of the separator\n      padding-right: var(--#{$prefix}breadcrumb-item-padding-x);\n      color: var(--#{$prefix}breadcrumb-divider-color);\n      content: var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider)) #{\"/* rtl:\"} var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider-flipped)) #{\"*/\"};\n    }\n  }\n\n  &.active {\n    color: var(--#{$prefix}breadcrumb-item-active-color);\n  }\n}\n",".pagination {\n  // scss-docs-start pagination-css-vars\n  --#{$prefix}pagination-padding-x: #{$pagination-padding-x};\n  --#{$prefix}pagination-padding-y: #{$pagination-padding-y};\n  @include rfs($pagination-font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-color: #{$pagination-color};\n  --#{$prefix}pagination-bg: #{$pagination-bg};\n  --#{$prefix}pagination-border-width: #{$pagination-border-width};\n  --#{$prefix}pagination-border-color: #{$pagination-border-color};\n  --#{$prefix}pagination-border-radius: #{$pagination-border-radius};\n  --#{$prefix}pagination-hover-color: #{$pagination-hover-color};\n  --#{$prefix}pagination-hover-bg: #{$pagination-hover-bg};\n  --#{$prefix}pagination-hover-border-color: #{$pagination-hover-border-color};\n  --#{$prefix}pagination-focus-color: #{$pagination-focus-color};\n  --#{$prefix}pagination-focus-bg: #{$pagination-focus-bg};\n  --#{$prefix}pagination-focus-box-shadow: #{$pagination-focus-box-shadow};\n  --#{$prefix}pagination-active-color: #{$pagination-active-color};\n  --#{$prefix}pagination-active-bg: #{$pagination-active-bg};\n  --#{$prefix}pagination-active-border-color: #{$pagination-active-border-color};\n  --#{$prefix}pagination-disabled-color: #{$pagination-disabled-color};\n  --#{$prefix}pagination-disabled-bg: #{$pagination-disabled-bg};\n  --#{$prefix}pagination-disabled-border-color: #{$pagination-disabled-border-color};\n  // scss-docs-end pagination-css-vars\n\n  display: flex;\n  @include list-unstyled();\n}\n\n.page-link {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}pagination-padding-y) var(--#{$prefix}pagination-padding-x);\n  @include font-size(var(--#{$prefix}pagination-font-size));\n  color: var(--#{$prefix}pagination-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}pagination-bg);\n  border: var(--#{$prefix}pagination-border-width) solid var(--#{$prefix}pagination-border-color);\n  @include transition($pagination-transition);\n\n  &:hover {\n    z-index: 2;\n    color: var(--#{$prefix}pagination-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}pagination-hover-bg);\n    border-color: var(--#{$prefix}pagination-hover-border-color);\n  }\n\n  &:focus {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-focus-color);\n    background-color: var(--#{$prefix}pagination-focus-bg);\n    outline: $pagination-focus-outline;\n    box-shadow: var(--#{$prefix}pagination-focus-box-shadow);\n  }\n\n  &.active,\n  .active > & {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-active-color);\n    @include gradient-bg(var(--#{$prefix}pagination-active-bg));\n    border-color: var(--#{$prefix}pagination-active-border-color);\n  }\n\n  &.disabled,\n  .disabled > & {\n    color: var(--#{$prefix}pagination-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}pagination-disabled-bg);\n    border-color: var(--#{$prefix}pagination-disabled-border-color);\n  }\n}\n\n.page-item {\n  &:not(:first-child) .page-link {\n    margin-left: $pagination-margin-start;\n  }\n\n  @if $pagination-margin-start == calc(#{$pagination-border-width} * -1) {\n    &:first-child {\n      .page-link {\n        @include border-start-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n\n    &:last-child {\n      .page-link {\n        @include border-end-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n  } @else {\n    // Add border-radius to all pageLinks in case they have left margin\n    .page-link {\n      @include border-radius(var(--#{$prefix}pagination-border-radius));\n    }\n  }\n}\n\n\n//\n// Sizing\n//\n\n.pagination-lg {\n  @include pagination-size($pagination-padding-y-lg, $pagination-padding-x-lg, $font-size-lg, $pagination-border-radius-lg);\n}\n\n.pagination-sm {\n  @include pagination-size($pagination-padding-y-sm, $pagination-padding-x-sm, $font-size-sm, $pagination-border-radius-sm);\n}\n","// Pagination\n\n// scss-docs-start pagination-mixin\n@mixin pagination-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}pagination-padding-x: #{$padding-x};\n  --#{$prefix}pagination-padding-y: #{$padding-y};\n  @include rfs($font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-border-radius: #{$border-radius};\n}\n// scss-docs-end pagination-mixin\n","// Base class\n//\n// Requires one of the contextual, color modifier classes for `color` and\n// `background-color`.\n\n.badge {\n  // scss-docs-start badge-css-vars\n  --#{$prefix}badge-padding-x: #{$badge-padding-x};\n  --#{$prefix}badge-padding-y: #{$badge-padding-y};\n  @include rfs($badge-font-size, --#{$prefix}badge-font-size);\n  --#{$prefix}badge-font-weight: #{$badge-font-weight};\n  --#{$prefix}badge-color: #{$badge-color};\n  --#{$prefix}badge-border-radius: #{$badge-border-radius};\n  // scss-docs-end badge-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}badge-padding-y) var(--#{$prefix}badge-padding-x);\n  @include font-size(var(--#{$prefix}badge-font-size));\n  font-weight: var(--#{$prefix}badge-font-weight);\n  line-height: 1;\n  color: var(--#{$prefix}badge-color);\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  @include border-radius(var(--#{$prefix}badge-border-radius));\n  @include gradient-bg();\n\n  // Empty badges collapse automatically\n  &:empty {\n    display: none;\n  }\n}\n\n// Quick fix for badges in buttons\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n","//\n// Base styles\n//\n\n.alert {\n  // scss-docs-start alert-css-vars\n  --#{$prefix}alert-bg: transparent;\n  --#{$prefix}alert-padding-x: #{$alert-padding-x};\n  --#{$prefix}alert-padding-y: #{$alert-padding-y};\n  --#{$prefix}alert-margin-bottom: #{$alert-margin-bottom};\n  --#{$prefix}alert-color: inherit;\n  --#{$prefix}alert-border-color: transparent;\n  --#{$prefix}alert-border: #{$alert-border-width} solid var(--#{$prefix}alert-border-color);\n  --#{$prefix}alert-border-radius: #{$alert-border-radius};\n  --#{$prefix}alert-link-color: inherit;\n  // scss-docs-end alert-css-vars\n\n  position: relative;\n  padding: var(--#{$prefix}alert-padding-y) var(--#{$prefix}alert-padding-x);\n  margin-bottom: var(--#{$prefix}alert-margin-bottom);\n  color: var(--#{$prefix}alert-color);\n  background-color: var(--#{$prefix}alert-bg);\n  border: var(--#{$prefix}alert-border);\n  @include border-radius(var(--#{$prefix}alert-border-radius));\n}\n\n// Headings for larger alerts\n.alert-heading {\n  // Specified to prevent conflicts of changing $headings-color\n  color: inherit;\n}\n\n// Provide class for links that match alerts\n.alert-link {\n  font-weight: $alert-link-font-weight;\n  color: var(--#{$prefix}alert-link-color);\n}\n\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissible {\n  padding-right: $alert-dismissible-padding-r;\n\n  // Adjust close link position\n  .btn-close {\n    position: absolute;\n    top: 0;\n    right: 0;\n    z-index: $stretched-link-z-index + 1;\n    padding: $alert-padding-y * 1.25 $alert-padding-x;\n  }\n}\n\n\n// scss-docs-start alert-modifiers\n// Generate contextual modifier classes for colorizing the alert\n@each $state in map-keys($theme-colors) {\n  .alert-#{$state} {\n    --#{$prefix}alert-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}alert-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}alert-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}alert-link-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end alert-modifiers\n","// Disable animation if transitions are disabled\n\n// scss-docs-start progress-keyframes\n@if $enable-transitions {\n  @keyframes progress-bar-stripes {\n    0% { background-position-x: $progress-height; }\n  }\n}\n// scss-docs-end progress-keyframes\n\n.progress,\n.progress-stacked {\n  // scss-docs-start progress-css-vars\n  --#{$prefix}progress-height: #{$progress-height};\n  @include rfs($progress-font-size, --#{$prefix}progress-font-size);\n  --#{$prefix}progress-bg: #{$progress-bg};\n  --#{$prefix}progress-border-radius: #{$progress-border-radius};\n  --#{$prefix}progress-box-shadow: #{$progress-box-shadow};\n  --#{$prefix}progress-bar-color: #{$progress-bar-color};\n  --#{$prefix}progress-bar-bg: #{$progress-bar-bg};\n  --#{$prefix}progress-bar-transition: #{$progress-bar-transition};\n  // scss-docs-end progress-css-vars\n\n  display: flex;\n  height: var(--#{$prefix}progress-height);\n  overflow: hidden; // force rounded corners by cropping it\n  @include font-size(var(--#{$prefix}progress-font-size));\n  background-color: var(--#{$prefix}progress-bg);\n  @include border-radius(var(--#{$prefix}progress-border-radius));\n  @include box-shadow(var(--#{$prefix}progress-box-shadow));\n}\n\n.progress-bar {\n  display: flex;\n  flex-direction: column;\n  justify-content: center;\n  overflow: hidden;\n  color: var(--#{$prefix}progress-bar-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--#{$prefix}progress-bar-bg);\n  @include transition(var(--#{$prefix}progress-bar-transition));\n}\n\n.progress-bar-striped {\n  @include gradient-striped();\n  background-size: var(--#{$prefix}progress-height) var(--#{$prefix}progress-height);\n}\n\n.progress-stacked > .progress {\n  overflow: visible;\n}\n\n.progress-stacked > .progress > .progress-bar {\n  width: 100%;\n}\n\n@if $enable-transitions {\n  .progress-bar-animated {\n    animation: $progress-bar-animation-timing progress-bar-stripes;\n\n    @if $enable-reduced-motion {\n      @media (prefers-reduced-motion: reduce) {\n        animation: none;\n      }\n    }\n  }\n}\n","// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // scss-docs-start list-group-css-vars\n  --#{$prefix}list-group-color: #{$list-group-color};\n  --#{$prefix}list-group-bg: #{$list-group-bg};\n  --#{$prefix}list-group-border-color: #{$list-group-border-color};\n  --#{$prefix}list-group-border-width: #{$list-group-border-width};\n  --#{$prefix}list-group-border-radius: #{$list-group-border-radius};\n  --#{$prefix}list-group-item-padding-x: #{$list-group-item-padding-x};\n  --#{$prefix}list-group-item-padding-y: #{$list-group-item-padding-y};\n  --#{$prefix}list-group-action-color: #{$list-group-action-color};\n  --#{$prefix}list-group-action-hover-color: #{$list-group-action-hover-color};\n  --#{$prefix}list-group-action-hover-bg: #{$list-group-hover-bg};\n  --#{$prefix}list-group-action-active-color: #{$list-group-action-active-color};\n  --#{$prefix}list-group-action-active-bg: #{$list-group-action-active-bg};\n  --#{$prefix}list-group-disabled-color: #{$list-group-disabled-color};\n  --#{$prefix}list-group-disabled-bg: #{$list-group-disabled-bg};\n  --#{$prefix}list-group-active-color: #{$list-group-active-color};\n  --#{$prefix}list-group-active-bg: #{$list-group-active-bg};\n  --#{$prefix}list-group-active-border-color: #{$list-group-active-border-color};\n  // scss-docs-end list-group-css-vars\n\n  display: flex;\n  flex-direction: column;\n\n  // No need to set list-style: none; since .list-group-item is block level\n  padding-left: 0; // reset padding because ul and ol\n  margin-bottom: 0;\n  @include border-radius(var(--#{$prefix}list-group-border-radius));\n}\n\n.list-group-numbered {\n  list-style-type: none;\n  counter-reset: section;\n\n  > .list-group-item::before {\n    // Increments only this instance of the section counter\n    content: counters(section, \".\") \". \";\n    counter-increment: section;\n  }\n}\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive\n// list items. Includes an extra `.active` modifier class for selected items.\n\n.list-group-item-action {\n  width: 100%; // For `<button>`s (anchors become 100% by default though)\n  color: var(--#{$prefix}list-group-action-color);\n  text-align: inherit; // For `<button>`s (anchors inherit)\n\n  // Hover state\n  &:hover,\n  &:focus {\n    z-index: 1; // Place hover/focus items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-action-hover-color);\n    text-decoration: none;\n    background-color: var(--#{$prefix}list-group-action-hover-bg);\n  }\n\n  &:active {\n    color: var(--#{$prefix}list-group-action-active-color);\n    background-color: var(--#{$prefix}list-group-action-active-bg);\n  }\n}\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}list-group-item-padding-y) var(--#{$prefix}list-group-item-padding-x);\n  color: var(--#{$prefix}list-group-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}list-group-bg);\n  border: var(--#{$prefix}list-group-border-width) solid var(--#{$prefix}list-group-border-color);\n\n  &:first-child {\n    @include border-top-radius(inherit);\n  }\n\n  &:last-child {\n    @include border-bottom-radius(inherit);\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}list-group-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}list-group-disabled-bg);\n  }\n\n  // Include both here for `<a>`s and `<button>`s\n  &.active {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-active-color);\n    background-color: var(--#{$prefix}list-group-active-bg);\n    border-color: var(--#{$prefix}list-group-active-border-color);\n  }\n\n  // stylelint-disable-next-line scss/selector-no-redundant-nesting-selector\n  & + .list-group-item {\n    border-top-width: 0;\n\n    &.active {\n      margin-top: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n      border-top-width: var(--#{$prefix}list-group-border-width);\n    }\n  }\n}\n\n// Horizontal\n//\n// Change the layout of list group items from vertical (default) to horizontal.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .list-group-horizontal#{$infix} {\n      flex-direction: row;\n\n      > .list-group-item {\n        &:first-child:not(:last-child) {\n          @include border-bottom-start-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-top-end-radius(0);\n        }\n\n        &:last-child:not(:first-child) {\n          @include border-top-end-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-bottom-start-radius(0);\n        }\n\n        &.active {\n          margin-top: 0;\n        }\n\n        + .list-group-item {\n          border-top-width: var(--#{$prefix}list-group-border-width);\n          border-left-width: 0;\n\n          &.active {\n            margin-left: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n            border-left-width: var(--#{$prefix}list-group-border-width);\n          }\n        }\n      }\n    }\n  }\n}\n\n\n// Flush list items\n//\n// Remove borders and border-radius to keep list group items edge-to-edge. Most\n// useful within other components (e.g., cards).\n\n.list-group-flush {\n  @include border-radius(0);\n\n  > .list-group-item {\n    border-width: 0 0 var(--#{$prefix}list-group-border-width);\n\n    &:last-child {\n      border-bottom-width: 0;\n    }\n  }\n}\n\n\n// scss-docs-start list-group-modifiers\n// List group contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n@each $state in map-keys($theme-colors) {\n  .list-group-item-#{$state} {\n    --#{$prefix}list-group-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-hover-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-hover-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-active-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-active-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-active-color: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-active-bg: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-active-border-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end list-group-modifiers\n","// Transparent background and border properties included for button version.\n// iOS requires the button element instead of an anchor tag.\n// If you want the anchor version, it requires `href=\"#\"`.\n// See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n.btn-close {\n  // scss-docs-start close-css-vars\n  --#{$prefix}btn-close-color: #{$btn-close-color};\n  --#{$prefix}btn-close-bg: #{ escape-svg($btn-close-bg) };\n  --#{$prefix}btn-close-opacity: #{$btn-close-opacity};\n  --#{$prefix}btn-close-hover-opacity: #{$btn-close-hover-opacity};\n  --#{$prefix}btn-close-focus-shadow: #{$btn-close-focus-shadow};\n  --#{$prefix}btn-close-focus-opacity: #{$btn-close-focus-opacity};\n  --#{$prefix}btn-close-disabled-opacity: #{$btn-close-disabled-opacity};\n  --#{$prefix}btn-close-white-filter: #{$btn-close-white-filter};\n  // scss-docs-end close-css-vars\n\n  box-sizing: content-box;\n  width: $btn-close-width;\n  height: $btn-close-height;\n  padding: $btn-close-padding-y $btn-close-padding-x;\n  color: var(--#{$prefix}btn-close-color);\n  background: transparent var(--#{$prefix}btn-close-bg) center / $btn-close-width auto no-repeat; // include transparent for button elements\n  border: 0; // for button elements\n  @include border-radius();\n  opacity: var(--#{$prefix}btn-close-opacity);\n\n  // Override <a>'s hover style\n  &:hover {\n    color: var(--#{$prefix}btn-close-color);\n    text-decoration: none;\n    opacity: var(--#{$prefix}btn-close-hover-opacity);\n  }\n\n  &:focus {\n    outline: 0;\n    box-shadow: var(--#{$prefix}btn-close-focus-shadow);\n    opacity: var(--#{$prefix}btn-close-focus-opacity);\n  }\n\n  &:disabled,\n  &.disabled {\n    pointer-events: none;\n    user-select: none;\n    opacity: var(--#{$prefix}btn-close-disabled-opacity);\n  }\n}\n\n@mixin btn-close-white() {\n  filter: var(--#{$prefix}btn-close-white-filter);\n}\n\n.btn-close-white {\n  @include btn-close-white();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .btn-close {\n      @include btn-close-white();\n    }\n  }\n}\n",".toast {\n  // scss-docs-start toast-css-vars\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n  --#{$prefix}toast-padding-x: #{$toast-padding-x};\n  --#{$prefix}toast-padding-y: #{$toast-padding-y};\n  --#{$prefix}toast-spacing: #{$toast-spacing};\n  --#{$prefix}toast-max-width: #{$toast-max-width};\n  @include rfs($toast-font-size, --#{$prefix}toast-font-size);\n  --#{$prefix}toast-color: #{$toast-color};\n  --#{$prefix}toast-bg: #{$toast-background-color};\n  --#{$prefix}toast-border-width: #{$toast-border-width};\n  --#{$prefix}toast-border-color: #{$toast-border-color};\n  --#{$prefix}toast-border-radius: #{$toast-border-radius};\n  --#{$prefix}toast-box-shadow: #{$toast-box-shadow};\n  --#{$prefix}toast-header-color: #{$toast-header-color};\n  --#{$prefix}toast-header-bg: #{$toast-header-background-color};\n  --#{$prefix}toast-header-border-color: #{$toast-header-border-color};\n  // scss-docs-end toast-css-vars\n\n  width: var(--#{$prefix}toast-max-width);\n  max-width: 100%;\n  @include font-size(var(--#{$prefix}toast-font-size));\n  color: var(--#{$prefix}toast-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}toast-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-border-color);\n  box-shadow: var(--#{$prefix}toast-box-shadow);\n  @include border-radius(var(--#{$prefix}toast-border-radius));\n\n  &.showing {\n    opacity: 0;\n  }\n\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.toast-container {\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n\n  position: absolute;\n  z-index: var(--#{$prefix}toast-zindex);\n  width: max-content;\n  max-width: 100%;\n  pointer-events: none;\n\n  > :not(:last-child) {\n    margin-bottom: var(--#{$prefix}toast-spacing);\n  }\n}\n\n.toast-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}toast-padding-y) var(--#{$prefix}toast-padding-x);\n  color: var(--#{$prefix}toast-header-color);\n  background-color: var(--#{$prefix}toast-header-bg);\n  background-clip: padding-box;\n  border-bottom: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-header-border-color);\n  @include border-top-radius(calc(var(--#{$prefix}toast-border-radius) - var(--#{$prefix}toast-border-width)));\n\n  .btn-close {\n    margin-right: calc(-.5 * var(--#{$prefix}toast-padding-x)); // stylelint-disable-line function-disallowed-list\n    margin-left: var(--#{$prefix}toast-padding-x);\n  }\n}\n\n.toast-body {\n  padding: var(--#{$prefix}toast-padding-x);\n  word-wrap: break-word;\n}\n","// stylelint-disable function-disallowed-list\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and stuff\n\n\n// Container that the modal scrolls within\n.modal {\n  // scss-docs-start modal-css-vars\n  --#{$prefix}modal-zindex: #{$zindex-modal};\n  --#{$prefix}modal-width: #{$modal-md};\n  --#{$prefix}modal-padding: #{$modal-inner-padding};\n  --#{$prefix}modal-margin: #{$modal-dialog-margin};\n  --#{$prefix}modal-color: #{$modal-content-color};\n  --#{$prefix}modal-bg: #{$modal-content-bg};\n  --#{$prefix}modal-border-color: #{$modal-content-border-color};\n  --#{$prefix}modal-border-width: #{$modal-content-border-width};\n  --#{$prefix}modal-border-radius: #{$modal-content-border-radius};\n  --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-xs};\n  --#{$prefix}modal-inner-border-radius: #{$modal-content-inner-border-radius};\n  --#{$prefix}modal-header-padding-x: #{$modal-header-padding-x};\n  --#{$prefix}modal-header-padding-y: #{$modal-header-padding-y};\n  --#{$prefix}modal-header-padding: #{$modal-header-padding}; // Todo in v6: Split this padding into x and y\n  --#{$prefix}modal-header-border-color: #{$modal-header-border-color};\n  --#{$prefix}modal-header-border-width: #{$modal-header-border-width};\n  --#{$prefix}modal-title-line-height: #{$modal-title-line-height};\n  --#{$prefix}modal-footer-gap: #{$modal-footer-margin-between};\n  --#{$prefix}modal-footer-bg: #{$modal-footer-bg};\n  --#{$prefix}modal-footer-border-color: #{$modal-footer-border-color};\n  --#{$prefix}modal-footer-border-width: #{$modal-footer-border-width};\n  // scss-docs-end modal-css-vars\n\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--#{$prefix}modal-zindex);\n  display: none;\n  width: 100%;\n  height: 100%;\n  overflow-x: hidden;\n  overflow-y: auto;\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n  // We deliberately don't use `-webkit-overflow-scrolling: touch;` due to a\n  // gnarly iOS Safari bug: https://bugs.webkit.org/show_bug.cgi?id=158342\n  // See also https://github.com/twbs/bootstrap/issues/17695\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: var(--#{$prefix}modal-margin);\n  // allow clicks to pass through for custom click handling to close modal\n  pointer-events: none;\n\n  // When fading in the modal, animate it to slide down\n  .modal.fade & {\n    @include transition($modal-transition);\n    transform: $modal-fade-transform;\n  }\n  .modal.show & {\n    transform: $modal-show-transform;\n  }\n\n  // When trying to close, animate focus to scale\n  .modal.modal-static & {\n    transform: $modal-scale-transform;\n  }\n}\n\n.modal-dialog-scrollable {\n  height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n\n  .modal-content {\n    max-height: 100%;\n    overflow: hidden;\n  }\n\n  .modal-body {\n    overflow-y: auto;\n  }\n}\n\n.modal-dialog-centered {\n  display: flex;\n  align-items: center;\n  min-height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  width: 100%; // Ensure `.modal-content` extends the full width of the parent `.modal-dialog`\n  // counteract the pointer-events: none; in the .modal-dialog\n  color: var(--#{$prefix}modal-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}modal-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}modal-border-width) solid var(--#{$prefix}modal-border-color);\n  @include border-radius(var(--#{$prefix}modal-border-radius));\n  @include box-shadow(var(--#{$prefix}modal-box-shadow));\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  // scss-docs-start modal-backdrop-css-vars\n  --#{$prefix}backdrop-zindex: #{$zindex-modal-backdrop};\n  --#{$prefix}backdrop-bg: #{$modal-backdrop-bg};\n  --#{$prefix}backdrop-opacity: #{$modal-backdrop-opacity};\n  // scss-docs-end modal-backdrop-css-vars\n\n  @include overlay-backdrop(var(--#{$prefix}backdrop-zindex), var(--#{$prefix}backdrop-bg), var(--#{$prefix}backdrop-opacity));\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  justify-content: space-between; // Put modal header elements (title and dismiss) on opposite ends\n  padding: var(--#{$prefix}modal-header-padding);\n  border-bottom: var(--#{$prefix}modal-header-border-width) solid var(--#{$prefix}modal-header-border-color);\n  @include border-top-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}modal-header-padding-y) * .5) calc(var(--#{$prefix}modal-header-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}modal-header-padding-y)) calc(-.5 * var(--#{$prefix}modal-header-padding-x)) calc(-.5 * var(--#{$prefix}modal-header-padding-y)) auto;\n  }\n}\n\n// Title text within header\n.modal-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}modal-title-line-height);\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  // Enable `flex-grow: 1` so that the body take up as much space as possible\n  // when there should be a fixed height on `.modal-dialog`.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}modal-padding);\n}\n\n// Footer (for actions)\n.modal-footer {\n  display: flex;\n  flex-shrink: 0;\n  flex-wrap: wrap;\n  align-items: center; // vertically center\n  justify-content: flex-end; // Right align buttons with flex property because text-align doesn't work on flex items\n  padding: calc(var(--#{$prefix}modal-padding) - var(--#{$prefix}modal-footer-gap) * .5);\n  background-color: var(--#{$prefix}modal-footer-bg);\n  border-top: var(--#{$prefix}modal-footer-border-width) solid var(--#{$prefix}modal-footer-border-color);\n  @include border-bottom-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  // Place margin between footer elements\n  // This solution is far from ideal because of the universal selector usage,\n  // but is needed to fix https://github.com/twbs/bootstrap/issues/24800\n  > * {\n    margin: calc(var(--#{$prefix}modal-footer-gap) * .5); // Todo in v6: replace with gap on parent class\n  }\n}\n\n// Scale up the modal\n@include media-breakpoint-up(sm) {\n  .modal {\n    --#{$prefix}modal-margin: #{$modal-dialog-margin-y-sm-up};\n    --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-sm-up};\n  }\n\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    max-width: var(--#{$prefix}modal-width);\n    margin-right: auto;\n    margin-left: auto;\n  }\n\n  .modal-sm {\n    --#{$prefix}modal-width: #{$modal-sm};\n  }\n}\n\n@include media-breakpoint-up(lg) {\n  .modal-lg,\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-lg};\n  }\n}\n\n@include media-breakpoint-up(xl) {\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-xl};\n  }\n}\n\n// scss-docs-start modal-fullscreen-loop\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n  $postfix: if($infix != \"\", $infix + \"-down\", \"\");\n\n  @include media-breakpoint-down($breakpoint) {\n    .modal-fullscreen#{$postfix} {\n      width: 100vw;\n      max-width: none;\n      height: 100%;\n      margin: 0;\n\n      .modal-content {\n        height: 100%;\n        border: 0;\n        @include border-radius(0);\n      }\n\n      .modal-header,\n      .modal-footer {\n        @include border-radius(0);\n      }\n\n      .modal-body {\n        overflow-y: auto;\n      }\n    }\n  }\n}\n// scss-docs-end modal-fullscreen-loop\n","// Shared between modals and offcanvases\n@mixin overlay-backdrop($zindex, $backdrop-bg, $backdrop-opacity) {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: $zindex;\n  width: 100vw;\n  height: 100vh;\n  background-color: $backdrop-bg;\n\n  // Fade for backdrop\n  &.fade { opacity: 0; }\n  &.show { opacity: $backdrop-opacity; }\n}\n","// Base class\n.tooltip {\n  // scss-docs-start tooltip-css-vars\n  --#{$prefix}tooltip-zindex: #{$zindex-tooltip};\n  --#{$prefix}tooltip-max-width: #{$tooltip-max-width};\n  --#{$prefix}tooltip-padding-x: #{$tooltip-padding-x};\n  --#{$prefix}tooltip-padding-y: #{$tooltip-padding-y};\n  --#{$prefix}tooltip-margin: #{$tooltip-margin};\n  @include rfs($tooltip-font-size, --#{$prefix}tooltip-font-size);\n  --#{$prefix}tooltip-color: #{$tooltip-color};\n  --#{$prefix}tooltip-bg: #{$tooltip-bg};\n  --#{$prefix}tooltip-border-radius: #{$tooltip-border-radius};\n  --#{$prefix}tooltip-opacity: #{$tooltip-opacity};\n  --#{$prefix}tooltip-arrow-width: #{$tooltip-arrow-width};\n  --#{$prefix}tooltip-arrow-height: #{$tooltip-arrow-height};\n  // scss-docs-end tooltip-css-vars\n\n  z-index: var(--#{$prefix}tooltip-zindex);\n  display: block;\n  margin: var(--#{$prefix}tooltip-margin);\n  @include deprecate(\"`$tooltip-margin`\", \"v5\", \"v5.x\", true);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}tooltip-font-size));\n  // Allow breaking very long words so they don't overflow the tooltip's bounds\n  word-wrap: break-word;\n  opacity: 0;\n\n  &.show { opacity: var(--#{$prefix}tooltip-opacity); }\n\n  .tooltip-arrow {\n    display: block;\n    width: var(--#{$prefix}tooltip-arrow-width);\n    height: var(--#{$prefix}tooltip-arrow-height);\n\n    &::before {\n      position: absolute;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n    }\n  }\n}\n\n.bs-tooltip-top .tooltip-arrow {\n  bottom: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    top: -1px;\n    border-width: var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-top-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-end .tooltip-arrow {\n  left: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    right: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-right-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-bottom .tooltip-arrow {\n  top: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    bottom: -1px;\n    border-width: 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-bottom-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-start .tooltip-arrow {\n  right: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    left: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-left-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-tooltip-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-tooltip-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-tooltip-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-tooltip-start;\n  }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: var(--#{$prefix}tooltip-max-width);\n  padding: var(--#{$prefix}tooltip-padding-y) var(--#{$prefix}tooltip-padding-x);\n  color: var(--#{$prefix}tooltip-color);\n  text-align: center;\n  background-color: var(--#{$prefix}tooltip-bg);\n  @include border-radius(var(--#{$prefix}tooltip-border-radius));\n}\n","@mixin reset-text {\n  font-family: $font-family-base;\n  // We deliberately do NOT reset font-size or overflow-wrap / word-wrap.\n  font-style: normal;\n  font-weight: $font-weight-normal;\n  line-height: $line-height-base;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n}\n",".popover {\n  // scss-docs-start popover-css-vars\n  --#{$prefix}popover-zindex: #{$zindex-popover};\n  --#{$prefix}popover-max-width: #{$popover-max-width};\n  @include rfs($popover-font-size, --#{$prefix}popover-font-size);\n  --#{$prefix}popover-bg: #{$popover-bg};\n  --#{$prefix}popover-border-width: #{$popover-border-width};\n  --#{$prefix}popover-border-color: #{$popover-border-color};\n  --#{$prefix}popover-border-radius: #{$popover-border-radius};\n  --#{$prefix}popover-inner-border-radius: #{$popover-inner-border-radius};\n  --#{$prefix}popover-box-shadow: #{$popover-box-shadow};\n  --#{$prefix}popover-header-padding-x: #{$popover-header-padding-x};\n  --#{$prefix}popover-header-padding-y: #{$popover-header-padding-y};\n  @include rfs($popover-header-font-size, --#{$prefix}popover-header-font-size);\n  --#{$prefix}popover-header-color: #{$popover-header-color};\n  --#{$prefix}popover-header-bg: #{$popover-header-bg};\n  --#{$prefix}popover-body-padding-x: #{$popover-body-padding-x};\n  --#{$prefix}popover-body-padding-y: #{$popover-body-padding-y};\n  --#{$prefix}popover-body-color: #{$popover-body-color};\n  --#{$prefix}popover-arrow-width: #{$popover-arrow-width};\n  --#{$prefix}popover-arrow-height: #{$popover-arrow-height};\n  --#{$prefix}popover-arrow-border: var(--#{$prefix}popover-border-color);\n  // scss-docs-end popover-css-vars\n\n  z-index: var(--#{$prefix}popover-zindex);\n  display: block;\n  max-width: var(--#{$prefix}popover-max-width);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}popover-font-size));\n  // Allow breaking very long words so they don't overflow the popover's bounds\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}popover-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-radius(var(--#{$prefix}popover-border-radius));\n  @include box-shadow(var(--#{$prefix}popover-box-shadow));\n\n  .popover-arrow {\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    height: var(--#{$prefix}popover-arrow-height);\n\n    &::before,\n    &::after {\n      position: absolute;\n      display: block;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n      border-width: 0;\n    }\n  }\n}\n\n.bs-popover-top {\n  > .popover-arrow {\n    bottom: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      bottom: 0;\n      border-top-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      bottom: var(--#{$prefix}popover-border-width);\n      border-top-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-end {\n  > .popover-arrow {\n    left: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      left: 0;\n      border-right-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      left: var(--#{$prefix}popover-border-width);\n      border-right-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-bottom {\n  > .popover-arrow {\n    top: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      top: 0;\n      border-bottom-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      top: var(--#{$prefix}popover-border-width);\n      border-bottom-color: var(--#{$prefix}popover-bg);\n    }\n  }\n\n  // This will remove the popover-header's border just below the arrow\n  .popover-header::before {\n    position: absolute;\n    top: 0;\n    left: 50%;\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    margin-left: calc(-.5 * var(--#{$prefix}popover-arrow-width)); // stylelint-disable-line function-disallowed-list\n    content: \"\";\n    border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-header-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-start {\n  > .popover-arrow {\n    right: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      right: 0;\n      border-left-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      right: var(--#{$prefix}popover-border-width);\n      border-left-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-popover-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-popover-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-popover-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-popover-start;\n  }\n}\n\n// Offset the popover to account for the popover arrow\n.popover-header {\n  padding: var(--#{$prefix}popover-header-padding-y) var(--#{$prefix}popover-header-padding-x);\n  margin-bottom: 0; // Reset the default from Reboot\n  @include font-size(var(--#{$prefix}popover-header-font-size));\n  color: var(--#{$prefix}popover-header-color);\n  background-color: var(--#{$prefix}popover-header-bg);\n  border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-top-radius(var(--#{$prefix}popover-inner-border-radius));\n\n  &:empty {\n    display: none;\n  }\n}\n\n.popover-body {\n  padding: var(--#{$prefix}popover-body-padding-y) var(--#{$prefix}popover-body-padding-x);\n  color: var(--#{$prefix}popover-body-color);\n}\n","// Notes on the classes:\n//\n// 1. .carousel.pointer-event should ideally be pan-y (to allow for users to scroll vertically)\n//    even when their scroll action started on a carousel, but for compatibility (with Firefox)\n//    we're preventing all actions instead\n// 2. The .carousel-item-start and .carousel-item-end is used to indicate where\n//    the active slide is heading.\n// 3. .active.carousel-item is the current slide.\n// 4. .active.carousel-item-start and .active.carousel-item-end is the current\n//    slide in its in-transition state. Only one of these occurs at a time.\n// 5. .carousel-item-next.carousel-item-start and .carousel-item-prev.carousel-item-end\n//    is the upcoming slide in transition.\n\n.carousel {\n  position: relative;\n}\n\n.carousel.pointer-event {\n  touch-action: pan-y;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n  @include clearfix();\n}\n\n.carousel-item {\n  position: relative;\n  display: none;\n  float: left;\n  width: 100%;\n  margin-right: -100%;\n  backface-visibility: hidden;\n  @include transition($carousel-transition);\n}\n\n.carousel-item.active,\n.carousel-item-next,\n.carousel-item-prev {\n  display: block;\n}\n\n.carousel-item-next:not(.carousel-item-start),\n.active.carousel-item-end {\n  transform: translateX(100%);\n}\n\n.carousel-item-prev:not(.carousel-item-end),\n.active.carousel-item-start {\n  transform: translateX(-100%);\n}\n\n\n//\n// Alternate transitions\n//\n\n.carousel-fade {\n  .carousel-item {\n    opacity: 0;\n    transition-property: opacity;\n    transform: none;\n  }\n\n  .carousel-item.active,\n  .carousel-item-next.carousel-item-start,\n  .carousel-item-prev.carousel-item-end {\n    z-index: 1;\n    opacity: 1;\n  }\n\n  .active.carousel-item-start,\n  .active.carousel-item-end {\n    z-index: 0;\n    opacity: 0;\n    @include transition(opacity 0s $carousel-transition-duration);\n  }\n}\n\n\n//\n// Left/right controls for nav\n//\n\n.carousel-control-prev,\n.carousel-control-next {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  z-index: 1;\n  // Use flex for alignment (1-3)\n  display: flex; // 1. allow flex styles\n  align-items: center; // 2. vertically center contents\n  justify-content: center; // 3. horizontally center contents\n  width: $carousel-control-width;\n  padding: 0;\n  color: $carousel-control-color;\n  text-align: center;\n  background: none;\n  border: 0;\n  opacity: $carousel-control-opacity;\n  @include transition($carousel-control-transition);\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    color: $carousel-control-color;\n    text-decoration: none;\n    outline: 0;\n    opacity: $carousel-control-hover-opacity;\n  }\n}\n.carousel-control-prev {\n  left: 0;\n  background-image: if($enable-gradients, linear-gradient(90deg, rgba($black, .25), rgba($black, .001)), null);\n}\n.carousel-control-next {\n  right: 0;\n  background-image: if($enable-gradients, linear-gradient(270deg, rgba($black, .25), rgba($black, .001)), null);\n}\n\n// Icons for within\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: $carousel-control-icon-width;\n  height: $carousel-control-icon-width;\n  background-repeat: no-repeat;\n  background-position: 50%;\n  background-size: 100% 100%;\n}\n\n/* rtl:options: {\n  \"autoRename\": true,\n  \"stringMap\":[ {\n    \"name\"    : \"prev-next\",\n    \"search\"  : \"prev\",\n    \"replace\" : \"next\"\n  } ]\n} */\n.carousel-control-prev-icon {\n  background-image: escape-svg($carousel-control-prev-icon-bg);\n}\n.carousel-control-next-icon {\n  background-image: escape-svg($carousel-control-next-icon-bg);\n}\n\n// Optional indicator pips/controls\n//\n// Add a container (such as a list) with the following class and add an item (ideally a focusable control,\n// like a button) with data-bs-target for each slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 2;\n  display: flex;\n  justify-content: center;\n  padding: 0;\n  // Use the .carousel-control's width as margin so we don't overlay those\n  margin-right: $carousel-control-width;\n  margin-bottom: 1rem;\n  margin-left: $carousel-control-width;\n\n  [data-bs-target] {\n    box-sizing: content-box;\n    flex: 0 1 auto;\n    width: $carousel-indicator-width;\n    height: $carousel-indicator-height;\n    padding: 0;\n    margin-right: $carousel-indicator-spacer;\n    margin-left: $carousel-indicator-spacer;\n    text-indent: -999px;\n    cursor: pointer;\n    background-color: $carousel-indicator-active-bg;\n    background-clip: padding-box;\n    border: 0;\n    // Use transparent borders to increase the hit area by 10px on top and bottom.\n    border-top: $carousel-indicator-hit-area-height solid transparent;\n    border-bottom: $carousel-indicator-hit-area-height solid transparent;\n    opacity: $carousel-indicator-opacity;\n    @include transition($carousel-indicator-transition);\n  }\n\n  .active {\n    opacity: $carousel-indicator-active-opacity;\n  }\n}\n\n\n// Optional captions\n//\n//\n\n.carousel-caption {\n  position: absolute;\n  right: (100% - $carousel-caption-width) * .5;\n  bottom: $carousel-caption-spacer;\n  left: (100% - $carousel-caption-width) * .5;\n  padding-top: $carousel-caption-padding-y;\n  padding-bottom: $carousel-caption-padding-y;\n  color: $carousel-caption-color;\n  text-align: center;\n}\n\n// Dark mode carousel\n\n@mixin carousel-dark() {\n  .carousel-control-prev-icon,\n  .carousel-control-next-icon {\n    filter: $carousel-dark-control-icon-filter;\n  }\n\n  .carousel-indicators [data-bs-target] {\n    background-color: $carousel-dark-indicator-active-bg;\n  }\n\n  .carousel-caption {\n    color: $carousel-dark-caption-color;\n  }\n}\n\n.carousel-dark {\n  @include carousel-dark();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    @if $color-mode-type == \"media-query\" {\n      .carousel {\n        @include carousel-dark();\n      }\n    } @else {\n      .carousel,\n      &.carousel {\n        @include carousel-dark();\n      }\n    }\n  }\n}\n","// scss-docs-start clearfix\n@mixin clearfix() {\n  &::after {\n    display: block;\n    clear: both;\n    content: \"\";\n  }\n}\n// scss-docs-end clearfix\n","//\n// Rotating border\n//\n\n.spinner-grow,\n.spinner-border {\n  display: inline-block;\n  width: var(--#{$prefix}spinner-width);\n  height: var(--#{$prefix}spinner-height);\n  vertical-align: var(--#{$prefix}spinner-vertical-align);\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 50%;\n  animation: var(--#{$prefix}spinner-animation-speed) linear infinite var(--#{$prefix}spinner-animation-name);\n}\n\n// scss-docs-start spinner-border-keyframes\n@keyframes spinner-border {\n  to { transform: rotate(360deg) #{\"/* rtl:ignore */\"}; }\n}\n// scss-docs-end spinner-border-keyframes\n\n.spinner-border {\n  // scss-docs-start spinner-border-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-border;\n  // scss-docs-end spinner-border-css-vars\n\n  border: var(--#{$prefix}spinner-border-width) solid currentcolor;\n  border-right-color: transparent;\n}\n\n.spinner-border-sm {\n  // scss-docs-start spinner-border-sm-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width-sm};\n  // scss-docs-end spinner-border-sm-css-vars\n}\n\n//\n// Growing circle\n//\n\n// scss-docs-start spinner-grow-keyframes\n@keyframes spinner-grow {\n  0% {\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n    transform: none;\n  }\n}\n// scss-docs-end spinner-grow-keyframes\n\n.spinner-grow {\n  // scss-docs-start spinner-grow-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-grow;\n  // scss-docs-end spinner-grow-css-vars\n\n  background-color: currentcolor;\n  opacity: 0;\n}\n\n.spinner-grow-sm {\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n}\n\n@if $enable-reduced-motion {\n  @media (prefers-reduced-motion: reduce) {\n    .spinner-border,\n    .spinner-grow {\n      --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed * 2};\n    }\n  }\n}\n","// stylelint-disable function-disallowed-list\n\n%offcanvas-css-vars {\n  // scss-docs-start offcanvas-css-vars\n  --#{$prefix}offcanvas-zindex: #{$zindex-offcanvas};\n  --#{$prefix}offcanvas-width: #{$offcanvas-horizontal-width};\n  --#{$prefix}offcanvas-height: #{$offcanvas-vertical-height};\n  --#{$prefix}offcanvas-padding-x: #{$offcanvas-padding-x};\n  --#{$prefix}offcanvas-padding-y: #{$offcanvas-padding-y};\n  --#{$prefix}offcanvas-color: #{$offcanvas-color};\n  --#{$prefix}offcanvas-bg: #{$offcanvas-bg-color};\n  --#{$prefix}offcanvas-border-width: #{$offcanvas-border-width};\n  --#{$prefix}offcanvas-border-color: #{$offcanvas-border-color};\n  --#{$prefix}offcanvas-box-shadow: #{$offcanvas-box-shadow};\n  --#{$prefix}offcanvas-transition: #{transform $offcanvas-transition-duration ease-in-out};\n  --#{$prefix}offcanvas-title-line-height: #{$offcanvas-title-line-height};\n  // scss-docs-end offcanvas-css-vars\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @extend %offcanvas-css-vars;\n  }\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @include media-breakpoint-down($next) {\n      position: fixed;\n      bottom: 0;\n      z-index: var(--#{$prefix}offcanvas-zindex);\n      display: flex;\n      flex-direction: column;\n      max-width: 100%;\n      color: var(--#{$prefix}offcanvas-color);\n      visibility: hidden;\n      background-color: var(--#{$prefix}offcanvas-bg);\n      background-clip: padding-box;\n      outline: 0;\n      @include box-shadow(var(--#{$prefix}offcanvas-box-shadow));\n      @include transition(var(--#{$prefix}offcanvas-transition));\n\n      &.offcanvas-start {\n        top: 0;\n        left: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-right: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(-100%);\n      }\n\n      &.offcanvas-end {\n        top: 0;\n        right: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-left: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(100%);\n      }\n\n      &.offcanvas-top {\n        top: 0;\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-bottom: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(-100%);\n      }\n\n      &.offcanvas-bottom {\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-top: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(100%);\n      }\n\n      &.showing,\n      &.show:not(.hiding) {\n        transform: none;\n      }\n\n      &.showing,\n      &.hiding,\n      &.show {\n        visibility: visible;\n      }\n    }\n\n    @if not ($infix == \"\") {\n      @include media-breakpoint-up($next) {\n        --#{$prefix}offcanvas-height: auto;\n        --#{$prefix}offcanvas-border-width: 0;\n        background-color: transparent !important; // stylelint-disable-line declaration-no-important\n\n        .offcanvas-header {\n          display: none;\n        }\n\n        .offcanvas-body {\n          display: flex;\n          flex-grow: 0;\n          padding: 0;\n          overflow-y: visible;\n          // Reset `background-color` in case `.bg-*` classes are used in offcanvas\n          background-color: transparent !important; // stylelint-disable-line declaration-no-important\n        }\n      }\n    }\n  }\n}\n\n.offcanvas-backdrop {\n  @include overlay-backdrop($zindex-offcanvas-backdrop, $offcanvas-backdrop-bg, $offcanvas-backdrop-opacity);\n}\n\n.offcanvas-header {\n  display: flex;\n  align-items: center;\n  justify-content: space-between;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}offcanvas-padding-y) * .5) calc(var(--#{$prefix}offcanvas-padding-x) * .5);\n    margin-top: calc(-.5 * var(--#{$prefix}offcanvas-padding-y));\n    margin-right: calc(-.5 * var(--#{$prefix}offcanvas-padding-x));\n    margin-bottom: calc(-.5 * var(--#{$prefix}offcanvas-padding-y));\n  }\n}\n\n.offcanvas-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}offcanvas-title-line-height);\n}\n\n.offcanvas-body {\n  flex-grow: 1;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n  overflow-y: auto;\n}\n",".placeholder {\n  display: inline-block;\n  min-height: 1em;\n  vertical-align: middle;\n  cursor: wait;\n  background-color: currentcolor;\n  opacity: $placeholder-opacity-max;\n\n  &.btn::before {\n    display: inline-block;\n    content: \"\";\n  }\n}\n\n// Sizing\n.placeholder-xs {\n  min-height: .6em;\n}\n\n.placeholder-sm {\n  min-height: .8em;\n}\n\n.placeholder-lg {\n  min-height: 1.2em;\n}\n\n// Animation\n.placeholder-glow {\n  .placeholder {\n    animation: placeholder-glow 2s ease-in-out infinite;\n  }\n}\n\n@keyframes placeholder-glow {\n  50% {\n    opacity: $placeholder-opacity-min;\n  }\n}\n\n.placeholder-wave {\n  mask-image: linear-gradient(130deg, $black 55%, rgba(0, 0, 0, (1 - $placeholder-opacity-min)) 75%, $black 95%);\n  mask-size: 200% 100%;\n  animation: placeholder-wave 2s linear infinite;\n}\n\n@keyframes placeholder-wave {\n  100% {\n    mask-position: -200% 0%;\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .text-bg-#{$color} {\n    color: color-contrast($value) if($enable-important-utilities, !important, null);\n    background-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}bg-opacity, 1)) if($enable-important-utilities, !important, null);\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .link-#{$color} {\n    color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n    text-decoration-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n    @if $link-shade-percentage != 0 {\n      &:hover,\n      &:focus {\n        $hover-color: if(color-contrast($value) == $color-contrast-light, shade-color($value, $link-shade-percentage), tint-color($value, $link-shade-percentage));\n        color: RGBA(#{to-rgb($hover-color)}, var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n        text-decoration-color: RGBA(to-rgb($hover-color), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n      }\n    }\n  }\n}\n\n// One-off special link helper as a bridge until v6\n.link-body-emphasis {\n  color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n  text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n  @if $link-shade-percentage != 0 {\n    &:hover,\n    &:focus {\n      color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, .75)) if($enable-important-utilities, !important, null);\n      text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, .75)) if($enable-important-utilities, !important, null);\n    }\n  }\n}\n",".focus-ring:focus {\n  outline: 0;\n  // By default, there is no `--bs-focus-ring-x`, `--bs-focus-ring-y`, or `--bs-focus-ring-blur`, but we provide CSS variables with fallbacks to initial `0` values\n  box-shadow: var(--#{$prefix}focus-ring-x, 0) var(--#{$prefix}focus-ring-y, 0) var(--#{$prefix}focus-ring-blur, 0) var(--#{$prefix}focus-ring-width) var(--#{$prefix}focus-ring-color);\n}\n",".icon-link {\n  display: inline-flex;\n  gap: $icon-link-gap;\n  align-items: center;\n  text-decoration-color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, .5));\n  text-underline-offset: $icon-link-underline-offset;\n  backface-visibility: hidden;\n\n  > .bi {\n    flex-shrink: 0;\n    width: $icon-link-icon-size;\n    height: $icon-link-icon-size;\n    fill: currentcolor;\n    @include transition($icon-link-icon-transition);\n  }\n}\n\n.icon-link-hover {\n  &:hover,\n  &:focus-visible {\n    > .bi {\n      transform: var(--#{$prefix}icon-link-transform, $icon-link-icon-transform);\n    }\n  }\n}\n","// Credit: Nicolas Gallagher and SUIT CSS.\n\n.ratio {\n  position: relative;\n  width: 100%;\n\n  &::before {\n    display: block;\n    padding-top: var(--#{$prefix}aspect-ratio);\n    content: \"\";\n  }\n\n  > * {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n  }\n}\n\n@each $key, $ratio in $aspect-ratios {\n  .ratio-#{$key} {\n    --#{$prefix}aspect-ratio: #{$ratio};\n  }\n}\n","// Shorthand\n\n.fixed-top {\n  position: fixed;\n  top: 0;\n  right: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n.fixed-bottom {\n  position: fixed;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n// Responsive sticky top and bottom\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .sticky#{$infix}-top {\n      position: sticky;\n      top: 0;\n      z-index: $zindex-sticky;\n    }\n\n    .sticky#{$infix}-bottom {\n      position: sticky;\n      bottom: 0;\n      z-index: $zindex-sticky;\n    }\n  }\n}\n","// scss-docs-start stacks\n.hstack {\n  display: flex;\n  flex-direction: row;\n  align-items: center;\n  align-self: stretch;\n}\n\n.vstack {\n  display: flex;\n  flex: 1 1 auto;\n  flex-direction: column;\n  align-self: stretch;\n}\n// scss-docs-end stacks\n","//\n// Visually hidden\n//\n\n.visually-hidden,\n.visually-hidden-focusable:not(:focus):not(:focus-within) {\n  @include visually-hidden();\n}\n","// stylelint-disable declaration-no-important\n\n// Hide content visually while keeping it accessible to assistive technologies\n//\n// See: https://www.a11yproject.com/posts/2013-01-11-how-to-hide-content/\n// See: https://kittygiraudel.com/2016/10/13/css-hide-and-seek/\n\n@mixin visually-hidden() {\n  width: 1px !important;\n  height: 1px !important;\n  padding: 0 !important;\n  margin: -1px !important; // Fix for https://github.com/twbs/bootstrap/issues/25686\n  overflow: hidden !important;\n  clip: rect(0, 0, 0, 0) !important;\n  white-space: nowrap !important;\n  border: 0 !important;\n\n  // Fix for positioned table caption that could become anonymous cells\n  &:not(caption) {\n    position: absolute !important;\n  }\n}\n\n// Use to only display content when it's focused, or one of its child elements is focused\n// (i.e. when focus is within the element/container that the class was applied to)\n//\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n\n@mixin visually-hidden-focusable() {\n  &:not(:focus):not(:focus-within) {\n    @include visually-hidden();\n  }\n}\n","//\n// Stretched link\n//\n\n.stretched-link {\n  &::#{$stretched-link-pseudo-element} {\n    position: absolute;\n    top: 0;\n    right: 0;\n    bottom: 0;\n    left: 0;\n    z-index: $stretched-link-z-index;\n    content: \"\";\n  }\n}\n","//\n// Text truncation\n//\n\n.text-truncate {\n  @include text-truncate();\n}\n","// Text truncate\n// Requires inline-block or block for proper styling\n\n@mixin text-truncate() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n",".vr {\n  display: inline-block;\n  align-self: stretch;\n  width: $vr-border-width;\n  min-height: 1em;\n  background-color: currentcolor;\n  opacity: $hr-opacity;\n}\n","// Utility generator\n// Used to generate utilities & print utilities\n@mixin generate-utility($utility, $infix: \"\", $is-rfs-media-query: false) {\n  $values: map-get($utility, values);\n\n  // If the values are a list or string, convert it into a map\n  @if type-of($values) == \"string\" or type-of(nth($values, 1)) != \"list\" {\n    $values: zip($values, $values);\n  }\n\n  @each $key, $value in $values {\n    $properties: map-get($utility, property);\n\n    // Multiple properties are possible, for example with vertical or horizontal margins or paddings\n    @if type-of($properties) == \"string\" {\n      $properties: append((), $properties);\n    }\n\n    // Use custom class if present\n    $property-class: if(map-has-key($utility, class), map-get($utility, class), nth($properties, 1));\n    $property-class: if($property-class == null, \"\", $property-class);\n\n    // Use custom CSS variable name if present, otherwise default to `class`\n    $css-variable-name: if(map-has-key($utility, css-variable-name), map-get($utility, css-variable-name), map-get($utility, class));\n\n    // State params to generate pseudo-classes\n    $state: if(map-has-key($utility, state), map-get($utility, state), ());\n\n    $infix: if($property-class == \"\" and str-slice($infix, 1, 1) == \"-\", str-slice($infix, 2), $infix);\n\n    // Don't prefix if value key is null (e.g. with shadow class)\n    $property-class-modifier: if($key, if($property-class == \"\" and $infix == \"\", \"\", \"-\") + $key, \"\");\n\n    @if map-get($utility, rfs) {\n      // Inside the media query\n      @if $is-rfs-media-query {\n        $val: rfs-value($value);\n\n        // Do not render anything if fluid and non fluid values are the same\n        $value: if($val == rfs-fluid-value($value), null, $val);\n      }\n      @else {\n        $value: rfs-fluid-value($value);\n      }\n    }\n\n    $is-css-var: map-get($utility, css-var);\n    $is-local-vars: map-get($utility, local-vars);\n    $is-rtl: map-get($utility, rtl);\n\n    @if $value != null {\n      @if $is-rtl == false {\n        /* rtl:begin:remove */\n      }\n\n      @if $is-css-var {\n        .#{$property-class + $infix + $property-class-modifier} {\n          --#{$prefix}#{$css-variable-name}: #{$value};\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            --#{$prefix}#{$css-variable-name}: #{$value};\n          }\n        }\n      } @else {\n        .#{$property-class + $infix + $property-class-modifier} {\n          @each $property in $properties {\n            @if $is-local-vars {\n              @each $local-var, $variable in $is-local-vars {\n                --#{$prefix}#{$local-var}: #{$variable};\n              }\n            }\n            #{$property}: $value if($enable-important-utilities, !important, null);\n          }\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            @each $property in $properties {\n              @if $is-local-vars {\n                @each $local-var, $variable in $is-local-vars {\n                  --#{$prefix}#{$local-var}: #{$variable};\n                }\n              }\n              #{$property}: $value if($enable-important-utilities, !important, null);\n            }\n          }\n        }\n      }\n\n      @if $is-rtl == false {\n        /* rtl:end:remove */\n      }\n    }\n  }\n}\n","// Loop over each breakpoint\n@each $breakpoint in map-keys($grid-breakpoints) {\n\n  // Generate media query if needed\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    // Loop over each utility property\n    @each $key, $utility in $utilities {\n      // The utility can be disabled with `false`, thus check if the utility is a map first\n      // Only proceed if responsive media queries are enabled or if it's the base media query\n      @if type-of($utility) == \"map\" and (map-get($utility, responsive) or $infix == \"\") {\n        @include generate-utility($utility, $infix);\n      }\n    }\n  }\n}\n\n// RFS rescaling\n@media (min-width: $rfs-mq-value) {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    @if (map-get($grid-breakpoints, $breakpoint) < $rfs-breakpoint) {\n      // Loop over each utility property\n      @each $key, $utility in $utilities {\n        // The utility can be disabled with `false`, thus check if the utility is a map first\n        // Only proceed if responsive media queries are enabled or if it's the base media query\n        @if type-of($utility) == \"map\" and map-get($utility, rfs) and (map-get($utility, responsive) or $infix == \"\") {\n          @include generate-utility($utility, $infix, true);\n        }\n      }\n    }\n  }\n}\n\n\n// Print utilities\n@media print {\n  @each $key, $utility in $utilities {\n    // The utility can be disabled with `false`, thus check if the utility is a map first\n    // Then check if the utility needs print styles\n    @if type-of($utility) == \"map\" and map-get($utility, print) == true {\n      @include generate-utility($utility, \"-print\");\n    }\n  }\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/2023.2/_static/styles/pydata-sphinx-theme.css b/2023.2/_static/styles/pydata-sphinx-theme.css
new file mode 100644
index 0000000000..7167797aad
--- /dev/null
+++ b/2023.2/_static/styles/pydata-sphinx-theme.css
@@ -0,0 +1,2 @@
+html{--pst-header-height:4rem;--pst-header-article-height:calc(var(--pst-header-height)*2/3);--pst-sidebar-secondary:17rem;--pst-font-size-base:1rem;--pst-font-size-h1:2.5rem;--pst-font-size-h2:2rem;--pst-font-size-h3:1.75rem;--pst-font-size-h4:1.5rem;--pst-font-size-h5:1.25rem;--pst-font-size-h6:1.1rem;--pst-font-size-milli:0.9rem;--pst-sidebar-font-size:0.9rem;--pst-sidebar-font-size-mobile:1.1rem;--pst-sidebar-header-font-size:1.2rem;--pst-sidebar-header-font-weight:600;--pst-admonition-font-weight-heading:600;--pst-font-weight-caption:300;--pst-font-weight-heading:400;--pst-font-family-base-system:-apple-system,BlinkMacSystemFont,Segoe UI,"Helvetica Neue",Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol;--pst-font-family-monospace-system:"SFMono-Regular",Menlo,Consolas,Monaco,Liberation Mono,Lucida 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+ 1rem)}body{background-color:var(--pst-color-background);color:var(--pst-color-text-base);display:flex;flex-direction:column;font-family:var(--pst-font-family-base);font-weight:400;line-height:1.65;min-height:100vh}body::-webkit-scrollbar-track{background:var(--pst-color-background)}p{color:var(--pst-color-text-base);font-size:1em;margin-bottom:1.15rem}p.rubric{border-bottom:1px solid var(--pst-color-border)}p.centered{text-align:center}a{word-wrap:break-word;color:var(--pst-color-link);text-decoration:underline;text-decoration-thickness:max(1px,.0625rem);text-underline-offset:.1578em}a:hover{text-decoration-skip:none;color:var(--pst-color-link-hover);text-decoration-skip-ink:none;text-decoration-thickness:max(3px,.1875rem,.12em)}a:active,a:visited{color:var(--pst-color-link)}a:visited:hover{color:var(--pst-color-link-hover)}a:focus-visible{outline:2px solid var(--pst-color-accent)}a.headerlink{color:var(--pst-color-secondary);font-size:.8em;margin-left:.2em;opacity:.7;padding:0 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details.toggle-details summary{border-left:3px solid var(--pst-color-primary)}html div.rendered_html html .jp-RenderedHTMLCommon table{table-layout:auto}html[data-theme=dark] .bd-content .nboutput .output_area.rendered_html{color:var(--pst-color-on-background);background-color:var(--pst-color-text-base);border-radius:.25rem;padding:.5rem}html[data-theme=dark] .bd-content .nboutput .output_area.stderr{background:var(--pst-color-danger)}div.nblast.container{margin-bottom:1rem}div.cell_output .output{max-width:100%;overflow-x:auto}html[data-theme=dark] .bd-content div.cell_output img,html[data-theme=dark] .bd-content div.cell_output .text_html{color:var(--pst-color-on-background);background-color:var(--pst-color-text-base);border-radius:.25rem;padding:.5rem}.bd-content div.cell_input{display:flex;flex-direction:column;justify-content:stretch}.bd-content div.cell_input,.bd-content div.output{border-radius:.25rem}.bd-content div.output table{table-layout:auto}html[data-theme=dark] .bd-content img.leaflet-tile.leaflet-tile-loaded{border-radius:0;padding:0}.bd-search-container div#search-results>h2{font-size:var(--pst-font-size-icon);margin-top:0}.bd-search-container div#search-results p.search-summary{color:var(--pst-color-text-muted)}.bd-search-container ul.search{margin:0;list-style:none}.bd-search-container ul.search li{background-image:none;padding:1rem 0;margin:1rem 0;border-top:1px solid var(--pst-color-text-muted)}.bd-search-container ul.search li>a{font-size:1.2em}.bd-search-container ul.search li div.context,.bd-search-container ul.search li p.context{color:var(--pst-color-text-base);margin:.5em 0 0 0}.bd-search-container ul.search li div.context a:before,.bd-search-container ul.search li p.context a:before{content:\"#\";padding-right:.2em;color:var(--pst-color-text-muted)}","html {\n  /*****************************************************************************\n  * Overall Layout Variables\n  */\n\n  // Header height will impact the top offset for many sections\n  // Article header is 66% of Header\n  --pst-header-height: 4rem;\n  --pst-header-article-height: calc(var(--pst-header-height) * 2 / 3);\n  --pst-sidebar-secondary: 17rem;\n}\n\n/*******************************************************************************\n* Breakpoints that trigger UI changes\n*\n* Note that media-breakpoint-down begins at the next highest level!\n* So we should choose a media-breakpoint-down one *lower* than when we want to start\n* example: media-breakpoint-up(md) and media-breakpoint-down(sm) trigger at the same time\n* ref: https://github.com/twbs/bootstrap/issues/31214\n*/\n$breakpoint-sidebar-primary: lg; // When we collapse the primary sidebar\n$breakpoint-sidebar-secondary: xl; // When we collapse the secondary sidebar\n$breakpoint-page-width: 88rem; // taken from sphinx-basic-ng, which we are ultimately going to inherit\n\n/*******************************************************************************\n* Define the animation behaviour\n*/\n$animation-time: 200ms;\n\n/*******************************************************************************\n* UI shaping and padding\n*/\n$admonition-border-radius: 0.25rem;\n","html {\n  /*****************************************************************************\n  * Font features used in this theme\n  */\n\n  // base font size - applied at body/html level\n  --pst-font-size-base: 1rem;\n\n  // heading font sizes based on bootstrap sizing\n  --pst-font-size-h1: 2.5rem;\n  --pst-font-size-h2: 2rem;\n  --pst-font-size-h3: 1.75rem;\n  --pst-font-size-h4: 1.5rem;\n  --pst-font-size-h5: 1.25rem;\n  --pst-font-size-h6: 1.1rem;\n\n  // smaller than heading font sizes\n  --pst-font-size-milli: 0.9rem;\n\n  // Sidebar styles\n  --pst-sidebar-font-size: 0.9rem;\n  --pst-sidebar-font-size-mobile: 1.1rem;\n  --pst-sidebar-header-font-size: 1.2rem;\n  --pst-sidebar-header-font-weight: 600;\n\n  // Admonition styles\n  --pst-admonition-font-weight-heading: 600;\n\n  // Font weights\n  --pst-font-weight-caption: 300;\n  --pst-font-weight-heading: 400;\n\n  // Font family\n  // These are adapted from https://systemfontstack.com/ */\n  --pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI,\n    \"Helvetica Neue\", Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji,\n    Segoe UI Symbol;\n  --pst-font-family-monospace-system: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n    Liberation Mono, Lucida Console, monospace;\n\n  --pst-font-family-base: var(--pst-font-family-base-system);\n  --pst-font-family-heading: var(--pst-font-family-base-system);\n  --pst-font-family-monospace: var(--pst-font-family-monospace-system);\n}\n","html {\n  /*****************************************************************************\n  * Icon\n  */\n\n  // Font size across all icons\n  --pst-font-size-icon: 1.5rem;\n\n  // Font Awesome default icons\n  --pst-icon-check-circle: \"\\f058\"; // fa-solid fa-circle-check\n  --pst-icon-info-circle: \"\\f05a\"; // fa-solid fa-circle-info\n  --pst-icon-exclamation-triangle: \"\\f071\"; // fa-solid fa-triangle-exclamation\n  --pst-icon-exclamation-circle: \"\\f06a\"; // fa-solid fa-circle-exclamation\n  --pst-icon-times-circle: \"\\f057\"; // fa-solid fa-circle-xmark\n  --pst-icon-lightbulb: \"\\f0eb\"; // fa-solid fa-lightbulb\n  --pst-icon-download: \"\\f019\"; // fa-solid fa-download\n  --pst-icon-angle-left: \"\\f104\"; // fa-solid fa-angle-left\n  --pst-icon-angle-right: \"\\f105\"; // fa-solid fa-angle-right\n  --pst-icon-external-link: \"\\f35d\"; // fa-solid fa-up-right-from-square\n  --pst-icon-search-minus: \"\\f010\"; // fa-solid fa-magnifying-glass-minus\n  --pst-icon-github: \"\\f09b\"; // fa-brands fa-github\n  --pst-icon-gitlab: \"\\f296\"; // fa-brands fa-gitlab\n  --pst-icon-share: \"\\f064\"; // fa-solid fa-share\n  --pst-icon-bell: \"\\f0f3\"; // fa-solid fa-bell\n  --pst-icon-pencil: \"\\f303\"; // fa-solid fa-pencil\n\n  // Bootstrap icons\n  --pst-breadcrumb-divider: \"\\f105\";\n}\n","html {\n  /*****************************************************************************\n  * Admonitions\n  **/\n\n  --pst-icon-admonition-default: var(--pst-icon-bell);\n  --pst-icon-admonition-note: var(--pst-icon-info-circle);\n  --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-error: var(--pst-icon-times-circle);\n  --pst-icon-admonition-hint: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-tip: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-seealso: var(--pst-icon-share);\n  --pst-icon-admonition-todo: var(--pst-icon-pencil);\n}\n","html {\n  /*****************************************************************************\n  * versionmodified\n  **/\n\n  --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle);\n}\n","html {\n  font-size: var(--pst-font-size-base);\n  scroll-padding-top: calc(var(--pst-header-height) + 1rem);\n}\n\nbody {\n  background-color: var(--pst-color-background);\n  font-family: var(--pst-font-family-base);\n  font-weight: 400;\n  line-height: 1.65;\n  color: var(--pst-color-text-base);\n  min-height: 100vh;\n  display: flex;\n  flex-direction: column;\n\n  // hack to avoid the black background on some browser including Safari\n  &::-webkit-scrollbar-track {\n    background: var(--pst-color-background);\n  }\n}\n\np {\n  margin-bottom: 1.15rem;\n  font-size: 1em;\n  color: var(--pst-color-text-base);\n\n  /* section header in docstring pages */\n  &.rubric {\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  &.centered {\n    text-align: center;\n  }\n}\n\na {\n  @include link-style-default;\n\n  // The # anchor that appears on hover over headings\n  &.headerlink {\n    color: var(--pst-color-secondary);\n    opacity: 0.7;\n    font-size: 0.8em;\n    padding: 0 4px 0 4px;\n    margin-left: 0.2em;\n    text-decoration: none;\n    transition: all 0.2s ease-out;\n    user-select: none;\n\n    &:hover {\n      opacity: 1;\n    }\n  }\n\n  // set up a icon next to the shorten links from github and gitlab\n  &.github,\n  &.gitlab {\n    &::before {\n      color: var(--pst-color-text-muted);\n      font: var(--fa-font-brands);\n      margin-right: 0.25rem;\n    }\n  }\n\n  &.github::before {\n    content: var(--pst-icon-github);\n  }\n\n  &.gitlab::before {\n    content: var(--pst-icon-gitlab);\n  }\n}\n\n.heading-style {\n  margin: 2.75rem 0 1.05rem;\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-heading);\n  line-height: 1.15;\n}\n\nh1 {\n  @extend .heading-style;\n  margin-top: 0;\n  font-size: var(--pst-font-size-h1);\n  color: var(--pst-heading-color);\n}\n\nh2 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h2);\n  color: var(--pst-heading-color);\n}\n\nh3 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h3);\n  color: var(--pst-heading-color);\n}\n\nh4 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h4);\n  color: var(--pst-heading-color);\n}\n\nh5 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h5);\n  color: var(--pst-color-text-base);\n}\n\nh6 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h6);\n  color: var(--pst-color-text-base);\n}\n\nsmall,\n.text_small {\n  font-size: var(--pst-font-size-milli);\n}\n\nhr {\n  border: 0;\n  border-top: 1px solid var(--pst-color-border);\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--pst-font-family-monospace);\n}\n\nkbd {\n  // use theme negative\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-muted);\n\n  // Compound keyboard elements will have nested kbd in them, so this prevents double lines\n  &:not(.compound) {\n    border: 1px solid var(--pst-color-border);\n    margin: 0 0.1rem;\n    padding: 0.1rem 0.4rem;\n    box-shadow: 1px 1px 1px var(--pst-color-shadow);\n  }\n}\n\ncode {\n  color: var(--pst-color-inline-code);\n}\n\npre {\n  margin: 1.5em 0 1.5em 0;\n  padding: 1rem;\n  background-color: var(--pst-color-surface);\n  color: var(--pst-color-text-base);\n  line-height: 1.2em;\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  .linenos {\n    // minimum opacity to make the line numbers WCAG AA conformant\n    opacity: 0.8;\n    padding-right: 10px;\n  }\n}\n\n// the back to top btn\n#pst-back-to-top {\n  z-index: $zindex-tooltip;\n  position: fixed;\n  display: none;\n  top: 80vh;\n  left: 50vw;\n  transform: translate(-50%);\n  color: var(--pst-color-secondary-text);\n  background-color: var(--pst-color-secondary);\n  border: none;\n}\n","/**\n * Consistent styling for links\n **/\n\n// Define some useful variables for links styling consistency\n//\n//  Thickness of the underline for links\n// the default will be either:\n//  - 1px\n// - 0.0625rem if it's thicker than 1px because the user has changed the text\n//   size in their browser\n$link-underline-thickness: unquote(\"max(1px, .0625rem)\") !default;\n// Offset of link underlines from text baseline\n// The default is 3px expressed as ems, as calculated against the default body\n// font size (on desktop).\n$link-underline-offset: 0.1578em !default;\n// Thickness of link underlines in hover state\n// The default for each link will be the thickest of the following:\n//  - 3px\n//  - 0.1875rem, if it's thicker than 3px because the user has changed the text\n//    size in their browser\n//  - 0.12em (relative to the link's text size)\n$link-hover-decoration-thickness: unquote(\"max(3px, .1875rem, .12em)\") !default;\n\n//  Ensures links have an underline decoration by default - needed to meet\n// WCAG SC 1.4.1\n@mixin link-decoration {\n  text-decoration: underline;\n\n  @if $link-underline-thickness {\n    text-decoration-thickness: $link-underline-thickness;\n  }\n\n  @if $link-underline-offset {\n    text-underline-offset: $link-underline-offset;\n  }\n}\n\n// Ensures links have an underline decoration on hover - distinct from the\n// default behaviour\n@mixin link-decoration-hover {\n  @if $link-hover-decoration-thickness {\n    text-decoration-thickness: $link-hover-decoration-thickness;\n    // Disable ink skipping on underlines on hover. Browsers haven't\n    // standardised on this part of the spec yet, so set both properties\n    text-decoration-skip-ink: none; // Chromium, Firefox\n    text-decoration-skip: none; // Safari\n  }\n}\n\n// Simple hover style - can be used alone or in conjunction with other mixins\n// Add the text underline and change in thickness on hover\n@mixin link-style-hover {\n  &:hover {\n    @include link-decoration;\n    @include link-decoration-hover;\n    color: var(--pst-color-link-hover);\n  }\n}\n\n// Default link styles\n//\n// Defines: default unvisited, visited, hover, and active.\n// TODO: @trallard to improve focus styles in subsequent PR\n@mixin link-style-default {\n  // So that really long links don't spill out of their container\n  word-wrap: break-word;\n\n  color: var(--pst-color-link);\n  @include link-decoration;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to add active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link);\n  }\n\n  // Visited should still be hoverable\n  &:visited {\n    color: var(--pst-color-link);\n    &:hover {\n      color: var(--pst-color-link-hover);\n    }\n  }\n  @include focus-indicator;\n}\n\n// Text link styles\n//\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation bar links.\n@mixin link-style-text {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n    @include link-decoration;\n    @include link-decoration-hover;\n  }\n  @include focus-indicator;\n}\n\n// Sidebar and TOC links\n//\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation the primary sidebar and table of contents.\n// Active and hover should work together rather than one overriding the other.\n@mixin link-sidebar {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    text-decoration: underline;\n    background-color: transparent;\n    color: var(--pst-color-link-hover);\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to update active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link-hover);\n  }\n}\n\n// Sidebar current page link styles\n//\n// Adds a vertical line on the left hand side of the link to indicate that\n// it's the current page. Note this is distinct from an active state.\n// Used on the primary sidebar and the TOC.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-sidebar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  @if $link-hover-decoration-thickness {\n    box-shadow: inset\n      $link-hover-decoration-thickness\n      0px\n      0px\n      var(--pst-color-primary);\n  }\n}\n\n// Navigation bar current page link styles\n//\n// Adds a bottom underline, this leaves enough space for the hover state without\n// cluttering the navbar.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-navbar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  @if $link-hover-decoration-thickness {\n    border-bottom: $link-hover-decoration-thickness\n      solid\n      var(--pst-color-primary);\n  }\n}\n\n// Navigation bar icon links hover styles\n//\n// Adds a bottom box-shadow - since there is no text we cannot use text-decoration\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin icon-navbar-hover {\n  &:hover {\n    color: var(--pst-color-link-hover);\n    @if $link-hover-decoration-thickness {\n      box-shadow: 0px\n        $link-hover-decoration-thickness\n        0px\n        var(--pst-color-link-hover);\n    }\n  }\n}\n\n// Focus indicator\n@mixin focus-indicator {\n  &:focus-visible {\n    outline: 2px solid var(--pst-color-accent);\n  }\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n$accordion-button-focus-border-color:     $input-focus-border-color !default;\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-active-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n","/***\n * Rules for the UX/UI of skip navigation link btn. \n *It's only visible to people\n * navigating with keyboard for accessibility purposes\n * ref: https://www.youtube.com/watch?v=VUR0I5mqq7I\n ***/\n\n.skip-link {\n  position: fixed;\n  top: 0;\n  left: 0;\n  right: 0;\n  text-align: center;\n  background-color: var(--pst-color-warning);\n  // Ensure we are using a WCAG conformant colour\n  color: var(--pst-color-warning-text) !important;\n  padding: 0.5rem;\n  z-index: $zindex-modal;\n  border-bottom: 1px solid var(--pst-color-border);\n\n  // This shows / hides the button\n  transform: translateY(-100%);\n  transition: transform 150ms ease-in-out;\n  &:focus {\n    transform: translateY(0%);\n    // ensure this is visible\n    outline: 3px solid $foundation-black;\n  }\n}\n",".bd-container {\n  flex-grow: 1;\n  display: flex;\n  justify-content: center;\n\n  .bd-container__inner {\n    display: flex;\n  }\n}\n\n.bd-page-width {\n  width: 100%;\n  @include media-breakpoint-up(lg) {\n    max-width: $breakpoint-page-width;\n  }\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n",".bd-header-version-warning,\n.bd-header-announcement {\n  min-height: 3rem;\n  width: 100%;\n  display: flex;\n  position: relative;\n  align-items: center;\n  justify-content: center;\n  text-align: center;\n  padding: 0.5rem 12.5%; // Horizontal padding so the width is 75%\n  // One breakpoint less than $breakpoint-sidebar-primary. See variables/_layout.scss for more info.\n  @include media-breakpoint-down(lg) {\n    // Announcements can take a bit more width on mobile\n    padding: 0.5rem 2%;\n  }\n\n  p {\n    font-weight: bold;\n    margin: 0;\n  }\n\n  &:after {\n    position: absolute;\n    width: 100%;\n    height: 100%;\n    left: 0;\n    top: 0;\n    content: \"\";\n    z-index: -1; // So it doesn't hover over the content\n  }\n\n  &:empty {\n    display: none;\n  }\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n}\n\n// Bg color is now defined in the theme color palette - using our secondary color\n.bd-header-announcement {\n  &:after {\n    background-color: var(--pst-color-secondary-bg);\n  }\n}\n\n.bd-header-version-warning {\n  &:after {\n    background-color: var(--pst-color-danger-bg);\n  }\n}\n","/**\n * Main content area\n */\n.bd-main {\n  flex-grow: 1;\n  flex-direction: column;\n  display: flex;\n  min-width: 0;\n\n  .bd-content {\n    display: flex;\n    justify-content: center;\n    height: 100%;\n    .bd-article-container {\n      justify-content: start;\n      display: flex;\n      flex-direction: column;\n      // Max-width is slightly more than the W3 since our docs often have images.\n      // We shoot for about 100 characters per line instead of 80.\n      // ref: https://www.w3.org/WAI/tutorials/page-structure/styling/#line-length\n      width: 100%;\n      max-width: 60em;\n      overflow-x: auto; // Prevent wide content from pushing off the secondary sidebar\n      padding: 1rem;\n      .bd-article {\n        // Give a bit more verticle spacing on wide screens\n        @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n          padding-top: 1.5rem;\n          padding-left: 2rem;\n        }\n      }\n    }\n  }\n}\n",".bd-footer {\n  width: 100%;\n  border-top: 1px solid var(--pst-color-border);\n\n  .bd-footer__inner {\n    display: flex;\n    flex-grow: 1;\n    padding: 1rem;\n    margin: auto;\n  }\n\n  .footer-items__start,\n  .footer-items__center,\n  .footer-items__end {\n    display: flex;\n    flex-direction: column;\n    gap: 0.5rem;\n    justify-content: center;\n    flex-grow: 1;\n  }\n\n  .footer-items__center {\n    text-align: center;\n  }\n\n  .footer-items__end {\n    text-align: end;\n  }\n\n  // So that paragraphs don't take up extra room\n  .footer-item p {\n    margin-bottom: 0;\n  }\n}\n",".bd-footer-article {\n  margin-top: auto;\n\n  .footer-article-items {\n    display: flex;\n    flex-direction: column;\n  }\n}\n",".bd-footer-content {\n  .footer-content-items {\n    display: flex;\n    flex-direction: column;\n    margin-top: auto;\n  }\n}\n","/**\n * Header at the top of the page\n * It includes the announcement bar and the navigation bar.\n */\n\n// Styling for the Icon links can be found in components/_icon-links.scss\n\n// If we want the shadow to only point downward in the future, set\n// box-shadow to: 0 0.125rem 0.25rem -0.125rem rgba(0, 0, 0, 0.11);\n.bd-header {\n  position: sticky;\n  top: 0;\n  z-index: $zindex-fixed;\n\n  // Overrides bootstrap\n  background: var(--pst-color-on-background) !important;\n  box-shadow: 0 0.125rem 0.25rem 0 var(--pst-color-shadow);\n\n  width: 100%;\n  padding: 0;\n  max-width: 100vw;\n  justify-content: center;\n  .bd-header__inner {\n    display: flex;\n    align-items: center;\n    height: fit-content;\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n\n  // These items will define the height of the header\n  .navbar-item {\n    height: var(--pst-header-height);\n    max-height: var(--pst-header-height);\n    display: flex;\n    align-items: center;\n  }\n\n  // Hide the header items on mobile\n  .navbar-header-items {\n    display: none;\n    flex-shrink: 1;\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: flex;\n      flex-grow: 1;\n      padding: 0 0 0 0.5rem;\n    }\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center,\n  .navbar-header-items__start {\n    display: flex;\n    align-items: center;\n    flex-flow: wrap;\n    // In case we wrap our items to multiple rows on small screens\n    row-gap: 0;\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center {\n    column-gap: 1rem;\n  }\n\n  // A little smaller because this is displayed by default on mobile\n  .navbar-header-items__start {\n    flex-shrink: 0;\n    margin-right: auto;\n    gap: 0.5rem;\n  }\n\n  .navbar-header-items__end {\n    // End navbar items should snap to the right\n    justify-content: end;\n  }\n\n  // Contains the navigation links within the navbar\n  .navbar-nav {\n    display: flex;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      // Center align on wide screens so the dropdown button is centered properly\n      align-items: center;\n    }\n\n    li a.nav-link {\n      @include link-style-text;\n    }\n\n    // Current page is always underlined in the navbar\n    > .current > .nav-link {\n      @include link-navbar-current;\n    }\n\n    // Dropdowns for the extra links\n    .dropdown {\n      button {\n        display: unset;\n        color: var(--pst-color-text-muted);\n        border: none;\n        @include link-style-hover;\n        @include focus-indicator;\n      }\n\n      .dropdown-menu {\n        z-index: $zindex-popover;\n        border: 1px solid var(--pst-color-border);\n        box-shadow: 0 0 0.3rem 0.1rem var(--pst-color-shadow);\n        background-color: var(--pst-color-on-background);\n        padding: 0.5rem 0;\n        margin: 0.5rem 0;\n        min-width: 20rem;\n\n        .dropdown-item {\n          // Give the items in the dropdown some breathing room but let the hit\n          // and hover area of the items extend to the edges of the menu\n          padding: 0.25rem 1.5rem;\n\n          // Override Bootstrap\n          &:focus:not(:hover):not(:active) {\n            background-color: inherit;\n          }\n        }\n\n        // Hide the menu unless show has been clicked\n        &:not(.show) {\n          display: none;\n        }\n      }\n    }\n  }\n}\n\n// inline the element in the navbar as long as they fit and use display block when collapsing\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  .navbar-center-items .navbar-item {\n    display: inline-block;\n  }\n}\n\n.nav-link {\n  @include link-style-hover;\n\n  // Override Bootstrap\n  transition: none;\n\n  &.nav-external:after {\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-external-link);\n    font-size: 0.75em;\n    margin-left: 0.3em;\n  }\n}\n\n.bd-navbar-elements li.nav-item i {\n  font-size: 0.7rem;\n  padding-left: 2px;\n  vertical-align: middle;\n}\n\n/**\n * Showing and hiding the sidebar toggle buttons and header items\n */\n\n// Hide the header items on mobile\n.bd-header {\n  // Toggle labels\n  label {\n    &.sidebar-toggle {\n      display: flex;\n      cursor: pointer;\n      font-size: var(--pst-font-size-icon);\n      align-items: center;\n      color: var(--pst-color-muted);\n      margin-bottom: 0;\n      padding-bottom: 0.25rem;\n    }\n\n    &.primary-toggle {\n      margin-right: 1rem;\n      @include media-breakpoint-up($breakpoint-sidebar-primary) {\n        display: none;\n      }\n    }\n\n    &.secondary-toggle {\n      margin-left: 1rem;\n      @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n        display: none;\n      }\n    }\n    @include icon-navbar-hover;\n    @include focus-indicator;\n  }\n\n  // Hide the navbar header items on mobile because they're in the sidebar\n  .navbar-header-items {\n    display: none;\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: inherit;\n    }\n  }\n}\n\n// THe elements next to the hamburger menu only show on narrow screens\n.navbar-persistent--mobile {\n  margin-left: auto;\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: none;\n  }\n}\n\n// The navbar-persistent content should only show on wide screens\n.navbar-persistent--container {\n  display: none;\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: flex;\n  }\n}\n",".header-article__inner {\n  display: flex;\n  padding: 0 0.5rem;\n\n  // The items define the height so that it disappears if there are no items\n  .header-article-item {\n    min-height: var(--pst-header-article-height);\n    height: var(--pst-header-article-height);\n  }\n\n  .header-article-items__start,\n  .header-article-items__end {\n    display: flex;\n    align-items: start;\n    gap: 0.5rem;\n  }\n\n  .header-article-items__end {\n    margin-left: auto;\n  }\n}\n","/**\n * The primary sidebar on the left.\n * e.g., between-pages navigation.\n */\n\n.bd-sidebar-primary {\n  display: flex;\n  flex-direction: column;\n  gap: 1rem;\n  max-height: calc(100vh - var(--pst-header-height));\n  position: sticky;\n  top: var(--pst-header-height);\n  @include make-col(3);\n\n  // Borders padding and whitespace\n  padding: 2rem 1rem 1rem 1rem;\n  border-right: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n\n  font-size: var(--pst-sidebar-font-size-mobile);\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  // override bootstrap when navlink are displayed in the sidebar\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n  }\n\n  &.no-sidebar {\n    border-right: 0;\n  }\n\n  &.hide-on-wide {\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  // Headers shouldn't be colored in the sidebars and some extensions add headers\n  h1,\n  h2,\n  h3,\n  h4 {\n    color: var(--pst-color-text-base);\n  }\n\n  .sidebar-primary-items__start,\n  .sidebar-primary-items__end {\n    .sidebar-primary-item {\n      padding: 0.5rem 0;\n    }\n  }\n\n  // Hide the sidebar header items on widescreen since they are visible in the header\n  .sidebar-header-items {\n    display: flex;\n    flex-direction: column;\n\n    .sidebar-header-items__title {\n      font-weight: var(--pst-sidebar-header-font-weight);\n      font-size: var(--pst-sidebar-header-font-size);\n      color: var(--pst-color-text-base);\n      margin-bottom: 0.5rem;\n    }\n\n    // The dropdown toggle for extra links just shows them all instead.\n    .nav-item.dropdown {\n      // On mobile, the dropdown behaves like any other link, no hiding\n      button {\n        display: none;\n      }\n\n      .dropdown-menu {\n        display: flex;\n        flex-direction: column;\n        padding: 0;\n        margin: 0;\n        border: none;\n        background-color: inherit;\n        font-size: inherit;\n      }\n    }\n\n    // Center header items get displayed vertically, end items are displayed horizontally\n    .sidebar-header-items__center {\n      display: flex;\n      flex-direction: column;\n    }\n\n    // Positioning end items\n    .sidebar-header-items__end {\n      display: flex;\n      align-items: center;\n      gap: 0.5rem;\n    }\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  .sidebar-primary-items__start {\n    // Add a border on mobile to separate it from the header sidebar area\n    border-top: 1px solid var(--pst-color-border);\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      border-top: none;\n    }\n  }\n\n  .sidebar-primary-items__end {\n    margin-top: auto;\n    margin-bottom: 1em;\n  }\n  .list-caption {\n    list-style: none;\n    padding-left: 0px;\n  }\n  li {\n    position: relative;\n    // If it has children, add a bit more padding to wrap the content to avoid\n    // overlapping with the <label>\n    &.has-children {\n      > .reference {\n        padding-right: 30px;\n      }\n    }\n  }\n  // Navigation item chevrons\n  label.toctree-toggle {\n    position: absolute;\n    top: 0;\n    right: 0;\n    height: 30px;\n    width: 30px;\n\n    cursor: pointer;\n\n    display: flex;\n    justify-content: center;\n    align-items: center;\n\n    &:hover {\n      background: var(--pst-color-surface);\n    }\n\n    i {\n      display: inline-block;\n      font-size: 0.75rem;\n      text-align: center;\n      &:hover {\n        color: var(--pst-color-primary);\n      }\n    }\n  }\n  .label-parts {\n    width: 100%;\n    height: 100%;\n    &:hover {\n      background: none;\n    }\n    i {\n      width: 30px;\n      position: absolute;\n      top: 0.3em; // aligning chevron with text\n      right: 0em; // aligning chevron to the right\n    }\n  }\n}\n\n/* Between-page links and captions */\nnav.bd-links {\n  margin-right: -1rem;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: block;\n  }\n\n  ul {\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  li > a {\n    display: block;\n    padding: 0.25rem 0.65rem;\n    @include link-sidebar;\n    box-shadow: none;\n\n    &.reference.external {\n      &:after {\n        font: var(--fa-font-solid);\n        content: var(--pst-icon-external-link);\n        font-size: 0.75em;\n        margin-left: 0.3em;\n      }\n    }\n  }\n\n  .current {\n    > a {\n      @include link-sidebar-current;\n      background-color: transparent;\n    }\n  }\n\n  // Title\n  p.bd-links__title {\n    font-size: var(--pst-sidebar-header-font-size);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n\n  // Toctree captions\n  p.caption {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    position: relative;\n    margin-top: 1.25rem;\n    margin-bottom: 0.5rem;\n    color: var(--pst-color-text-base);\n    &:first-child {\n      margin-top: 0;\n    }\n\n    font-size: var(--pst-sidebar-font-size-mobile);\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is and invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","/**\n * Secondary sidebar on the right.\n * e.g., in-page table of contents.\n */\n.bd-sidebar-secondary {\n  display: flex;\n  order: 2;\n  flex-shrink: 0;\n  flex-direction: column;\n  position: sticky;\n  top: var(--pst-header-height);\n  max-height: calc(100vh - var(--pst-header-height));\n  padding: 2rem 1rem 1rem 1rem;\n  width: var(--pst-sidebar-secondary);\n  font-size: var(--pst-sidebar-font-size-mobile);\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  // Color and border\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n}\n\n.sidebar-secondary-item {\n  padding: 0.5rem 0.5rem;\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    border-left: 1px solid var(--pst-color-border);\n    padding-left: 1rem;\n  }\n\n  i {\n    padding-right: 0.5rem;\n  }\n}\n","/*******************************************************************************\n* Rules for the UX/UI of sidebar sliding drawers on mobile\n* Note that this sheet controls styles across many parts of the theme\n* It is aggregated into this one sheet instead of being split across\n* components in order to keep it easier to debug in one place.\n* It is broken up into major sections below.\n*/\n\n/*******************************************************************************\n* Buttons and overlays\n*/\ninput.sidebar-toggle {\n  display: none;\n}\n\n// Background overlays\nlabel.overlay {\n  background-color: black;\n  opacity: 0.5;\n  height: 0;\n  width: 0;\n  position: fixed;\n  top: 0;\n  left: 0;\n  transition: opacity $animation-time ease-out;\n  z-index: $zindex-modal-backdrop;\n}\n\ninput {\n  // Show the correct overlay when its input is checked\n  &#__primary:checked + label.overlay.overlay-primary,\n  &#__secondary:checked + label.overlay.overlay-secondary {\n    height: 100vh;\n    width: 100vw;\n  }\n\n  // Primary sidebar slides in from the left\n  &#__primary:checked ~ .bd-container .bd-sidebar-primary {\n    visibility: visible;\n    margin-left: 0;\n  }\n\n  // Secondary sidebar slides in from the right\n  &#__secondary:checked ~ .bd-container .bd-sidebar-secondary {\n    visibility: visible;\n    margin-right: 0;\n  }\n}\n\n/*******************************************************************************\n* Sidebar drawer behavior\n*/\n\n/**\n * Behavior for sliding drawer elements that will be toggled with an input\n *\n * NOTE: We use this mixin to define the toggle behavior on narrow screens,\n * And the wide-screen behavior of the sections is defined in their own section\n * .scss files.\n */\n@mixin sliding-drawer($side: \"left\") {\n  position: fixed;\n  top: 0;\n  z-index: $zindex-modal;\n  height: 100vh;\n  max-height: 100vh;\n  width: 75%;\n  flex-grow: 0.75;\n  max-width: 350px;\n  transition: visibility $animation-time ease-out,\n    margin $animation-time ease-out;\n  visibility: hidden;\n\n  @if $side == \"right\" {\n    margin-right: -75%;\n    right: 0;\n  } @else {\n    margin-left: -75%;\n    left: 0;\n  }\n}\n\n// Primary sidebar hides/shows at earlier widths\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  label.sidebar-toggle.primary-toggle {\n    display: none;\n  }\n\n  input#__primary {\n    &:checked + label.overlay.overlay-primary {\n      height: 0;\n      width: 0;\n    }\n  }\n\n  .bd-sidebar-primary {\n    margin-left: 0;\n    visibility: visible;\n  }\n}\n\n.bd-sidebar-primary {\n  @include media-breakpoint-down($breakpoint-sidebar-primary) {\n    @include sliding-drawer(\"left\");\n  }\n}\n\n.bd-sidebar-secondary {\n  @include media-breakpoint-down($breakpoint-sidebar-secondary) {\n    @include sliding-drawer(\"right\");\n  }\n}\n","/**\n * Breadcrumbs for parent pages meant for the article header\n */\nul.bd-breadcrumbs {\n  list-style: none;\n  padding-left: 0;\n  display: flex;\n  flex-wrap: wrap;\n\n  // Font size slightly smaller to avoid cluttering up space too much\n  font-size: 0.8rem;\n\n  li.breadcrumb-item {\n    display: flex;\n    align-items: baseline;\n\n    // Style should look like heavier in-page links\n    // keeping visited in the default link colour\n    font-weight: bold;\n    a {\n      @include link-style-text;\n    }\n\n    // Items that aren't the home have a caret to the left\n    &:not(.breadcrumb-home):before {\n      font: var(--fa-font-solid);\n      font-size: 0.8rem;\n      content: var(--pst-breadcrumb-divider);\n      color: var(--pst-color-text-muted);\n      padding: 0 0.5rem;\n    }\n  }\n}\n","/**\n * Icon links in the navbar\n */\n\n.navbar-icon-links {\n  display: flex;\n  flex-direction: row;\n  column-gap: 1rem;\n  flex-wrap: wrap;\n\n  // Remove the padding so that we can define it with flexbox gap above\n  li.nav-item a.nav-link {\n    padding-left: 0;\n    padding-right: 0;\n    @include icon-navbar-hover;\n  }\n\n  // Spacing and centering\n  a span {\n    display: flex;\n    align-items: center;\n  }\n\n  // Icons styling\n  i {\n    &.fa-brands,\n    &.fa-regular,\n    &.fa-solid {\n      vertical-align: middle;\n      font-style: normal;\n      font-size: var(--pst-font-size-icon);\n    }\n\n    /* Social media buttons hard-code the brand color */\n    &.fa-square-twitter:before {\n      color: #55acee;\n    }\n\n    &.fa-square-gitlab:before {\n      color: #548;\n    }\n\n    &.fa-bitbucket:before {\n      color: #0052cc;\n    }\n  }\n\n  // Force images to be icon-sized\n  img.icon-link-image {\n    height: 1.5em;\n    border-radius: 0.2rem;\n  }\n}\n","/**\n * Logo in the navbar\n */\n\n.navbar-brand {\n  position: relative;\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  padding: 0.5rem 0;\n  width: auto;\n  margin: 0;\n  display: flex;\n  // Ensure that the logo stays the same length while other content shrinks\n  flex-shrink: 0;\n  align-items: center;\n  gap: 0.5rem;\n\n  // If there's no logo image, we use a p element w/ the site title\n  p {\n    margin-bottom: 0;\n  }\n\n  // If there's a logo, it'll be in an img block\n  img {\n    max-width: 100%;\n    height: 100%;\n    width: auto;\n  }\n\n  // remove underline from brand title on default state\n  a {\n    text-decoration: none;\n  }\n  &:hover {\n    @include link-style-hover;\n  }\n  &:visited {\n    &:hover {\n      @include link-style-hover;\n    }\n  }\n}\n","/**\n * Navigation text links in the navbar\n */\n.navbar-nav {\n  ul {\n    display: block;\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  // Navbar links - do not have an underline by default\n  li {\n    display: flex;\n    flex-direction: column;\n\n    a {\n      display: flex;\n      align-items: center;\n      height: 100%;\n      padding-top: 0.25rem;\n      padding-bottom: 0.25rem;\n      @include link-style-text;\n    }\n  }\n\n  /**\n   * Togglable expand/collapse\n   * This is only applicable to the primary sidebar which has these checkboxes\n   */\n  .toctree-checkbox {\n    position: absolute;\n    display: none;\n  }\n\n  .toctree-checkbox {\n    ~ ul {\n      display: none;\n    }\n    ~ label i {\n      transform: rotate(0deg);\n    }\n  }\n  .toctree-checkbox:checked {\n    ~ ul {\n      display: block;\n    }\n    ~ label i {\n      transform: rotate(180deg);\n    }\n  }\n}\n\n// Don't display the `site navigation` in the header menu\n.bd-header .navbar-nav > p.sidebar-header-items__title {\n  display: none;\n}\n","/**\n * The list of in-page TOC links\n */\n.page-toc {\n  .section-nav {\n    padding-left: 0;\n    border-bottom: none;\n\n    ul {\n      padding-left: 1rem;\n    }\n  }\n\n  // override bootstrap settings\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n\n  .onthispage {\n    color: var(--pst-color-text-base);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n}\n","/**\n* Previous / Next navigation buttons\n**/\n.prev-next-area {\n  width: 100%;\n\n  p {\n    margin: 0 0.3em;\n    line-height: 1.3em;\n  }\n\n  i {\n    font-size: 1.2em;\n  }\n\n  a {\n    // So that buttons align with icons\n    display: flex;\n    align-items: center;\n    border: none;\n    padding: 10px;\n    max-width: 45%;\n    overflow-x: hidden;\n    color: var(--pst-color-text-muted);\n    text-decoration: none;\n\n    p.prev-next-title {\n      @include link-style-default;\n      font-weight: var(--pst-admonition-font-weight-heading);\n      font-size: 1.1em;\n    }\n\n    &:hover p.prev-next-title {\n      @include link-style-hover;\n    }\n    // Exception here - keep visited in the default link colour\n    // it still should hover on the default link hover colour\n    &:visited p.prev-next-title {\n      color: var(--pst-color-link);\n      &:hover {\n        color: var(--pst-color-link-hover);\n      }\n    }\n\n    .prev-next-info {\n      flex-direction: column;\n      margin: 0 0.5em;\n\n      .prev-next-subtitle {\n        text-transform: capitalize;\n      }\n    }\n\n    &.left-prev {\n      float: left;\n    }\n\n    &.right-next {\n      float: right;\n\n      div.prev-next-info {\n        text-align: right;\n      }\n    }\n  }\n}\n","/**\n * Search field\n **/\n.bd-search {\n  position: relative;\n  padding-left: 0.5rem;\n  gap: 0.5rem;\n  background-color: var(--pst-color-background);\n  border-radius: $admonition-border-radius;\n  border: 1px solid var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Background should always be same color regardless of active or not\n  &:active {\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n\n  .icon {\n    position: absolute;\n    color: var(--pst-color-border);\n    left: 25px;\n  }\n\n  .fa-solid.fa-magnifying-glass {\n    position: absolute;\n    left: calc((2.5rem - 0.7em) / 2);\n    color: var(--pst-color-text-muted);\n  }\n\n  input {\n    // Inner-text of the search bar\n    &::placeholder {\n      color: var(--pst-color-text-muted);\n    }\n\n    // Remove the little \"x\" that pops up when you start typing\n    &::-webkit-search-cancel-button,\n    &::-webkit-search-decoration {\n      -webkit-appearance: none;\n      appearance: none;\n    }\n  }\n\n  // Shows off the keyboard shortcuts for the button\n  .search-button__kbd-shortcut {\n    display: flex;\n    position: absolute;\n    right: 0.5rem;\n    color: var(--pst-color-border);\n  }\n}\n\n.form-control {\n  background-color: var(--pst-color-background);\n  color: var(--pst-color-text-base);\n\n  &:focus,\n  &:focus-visible {\n    border: none;\n    box-shadow: none;\n    outline: 3px solid var(--pst-color-accent);\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n}\n\n/**\n * Search button - located in the navbar\n */\n\n// Search link icon should be a bit bigger since it is separate from icon links\n.search-button {\n  display: flex;\n  align-items: center;\n  align-content: center;\n  color: var(--pst-color-text-muted);\n  // Needed to match other icons hover\n  padding: 0 0 0.25rem 0;\n  border-radius: 0;\n  @include icon-navbar-hover;\n  @include focus-indicator;\n\n  i {\n    font-size: 1.3rem;\n  }\n}\n\n// __search-container will only show up when we use the search pop-up bar\n.search-button__search-container,\n.search-button__overlay {\n  display: none;\n}\n.search-button__wrapper.show {\n  .search-button__search-container {\n    display: flex;\n    // Center in middle of screen just underneath header\n    position: fixed;\n    z-index: $zindex-modal;\n    top: 30%;\n    left: 50%;\n    transform: translate(-50%, -50%);\n    right: 1rem;\n    margin-top: 0.5rem;\n    width: 90%;\n    max-width: 800px;\n  }\n\n  .search-button__overlay {\n    display: flex;\n    position: fixed;\n    z-index: $zindex-modal-backdrop;\n    background-color: black;\n    opacity: 0.5;\n    width: 100%;\n    height: 100%;\n    top: 0px;\n    left: 0px;\n  }\n\n  form.bd-search {\n    flex-grow: 1;\n    padding-top: 0;\n    padding-bottom: 0;\n  }\n\n  // Font and input text a bit bigger\n  svg,\n  input {\n    font-size: var(--pst-font-size-icon);\n  }\n}\n\n/**\n * The search button component that looks like a field.\n * Lives at components/search-button-field.html\n */\n.search-button-field {\n  display: inline-flex;\n  align-items: center;\n  border: var(--pst-color-border) solid 1px;\n  border-radius: 1.5em;\n  color: var(--pst-color-text-muted);\n  padding: 0.5em;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    border: 2px solid var(--pst-color-link-hover);\n  }\n  &:focus-visible {\n    border: 2px solid var(--pst-color-accent);\n  }\n\n  // The keyboard shotcut text\n  .search-button__default-text {\n    font-size: var(--bs-nav-link-font-size);\n    font-weight: var(--bs-nav-link-font-weight);\n    margin-right: 0.5em;\n    margin-left: 0.5em;\n  }\n\n  .kbd-shortcut__modifier {\n    font-size: 0.75em;\n  }\n\n  // Ensures that all the text lines up in the middle\n  > * {\n    align-items: center;\n  }\n\n  // Only the icon should be visible on narrow screens\n  > :not(svg) {\n    display: none;\n\n    @include media-breakpoint-up(lg) {\n      display: flex;\n    }\n  }\n}\n","/**\n * The 'Hide Search Matches' button.\n * This only shows up when a person lands on a page after clicking a search result.\n * Clicking it removes the highlighting of the search term from the page.\n * We want it to behave like a button.\n */\ndiv#searchbox {\n  // Leave `#searchbox` rules empty so that it doesn't show at all when it is empty\n  p.highlight-link {\n    margin: 1rem 0;\n    width: fit-content;\n\n    // A bit more margin on wide screens to mimic article behavior\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      margin-left: 2rem;\n    }\n\n    // Put outer shadow on this one so that we can darken the link w/ an inner shadow\n    @include box-shadow();\n\n    // Style the button to look like a Sphinx Design button\n    a {\n      border-radius: 0.25rem;\n      font-size: 1.25rem;\n      padding: 0.75rem;\n      background-color: var(--pst-color-primary);\n      color: var(--pst-color-primary-text);\n      text-decoration: none;\n\n      // The box shadow is inset so that it darkens the button on hover\n      transition: box-shadow 0.25s ease-out;\n      &:hover {\n        box-shadow: inset 0px 0px 50px 50px rgba(0, 0, 0, 0.25);\n      }\n\n      &:before {\n        content: var(--pst-icon-search-minus);\n        color: unset;\n        font: var(--fa-font-solid);\n        margin-right: 0.5rem;\n      }\n    }\n  }\n}\n","/*********************************************\n* SASS Mixins\n*********************************************/\n\n/**\n * A consistent box shadow style we apply across elements.\n */\n@mixin box-shadow() {\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow),\n    0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n\n/**\n   * Set background of some cell outputs to white-ish to make sure colors work\n   * This is because many libraries make output that only looks good on white\n   */\n@mixin cell-output-background {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\n","/**\n * Light/dark theme switcher\n */\n\n.theme-switch-button {\n  // overide bootstrap settings\n  margin: 0 -0.5rem;\n  padding: 0; // We pad the `span` not the container\n  color: var(--pst-color-text-muted);\n  border-radius: 0;\n  @include focus-indicator;\n\n  span {\n    display: none;\n    padding: 0.5em;\n\n    @include icon-navbar-hover;\n    &:active {\n      text-decoration: none;\n      color: var(--pst-color-link-hover);\n    }\n  }\n}\n\nhtml[data-mode=\"auto\"] .theme-switch-button span[data-mode=\"auto\"] {\n  display: flex;\n}\nhtml[data-mode=\"light\"] .theme-switch-button span[data-mode=\"light\"] {\n  display: flex;\n}\nhtml[data-mode=\"dark\"] .theme-switch-button span[data-mode=\"dark\"] {\n  display: flex;\n}\n","button.btn.version-switcher__button {\n  border-color: var(--pst-color-border);\n  color: var(--pst-color-text-base);\n  // Add a margin on narrow screens to avoid feeling cramped\n  margin-bottom: 1em;\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    margin-bottom: unset;\n  }\n\n  @include link-style-hover;\n  @include focus-indicator;\n  &:active {\n    color: var(--pst-color-text-base);\n    border-color: var(--pst-color-border);\n  }\n}\n\n.version-switcher__menu {\n  border-color: var(--pst-color-border);\n  border-radius: var(--bs-dropdown-border-radius);\n\n  a.list-group-item {\n    background-color: var(--pst-color-on-background);\n    color: var(--pst-color-text-base);\n    padding: 0.75rem 1.25rem;\n\n    &:not(:last-child) {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n    @include link-style-hover;\n    &:hover {\n      background-color: var(--pst-color-surface);\n    }\n    &.active {\n      @include link-sidebar-current;\n      position: relative;\n      z-index: 1;\n\n      span:before {\n        content: \"\";\n        width: 100%;\n        height: 100%;\n        position: absolute;\n        z-index: -1;\n        left: 0;\n        top: 0;\n      }\n    }\n  }\n}\n\n// Font behavior on mobile\nbutton.version-switcher__button,\n.version-switcher__menu {\n  font-size: 1.1em; // A bit smaller than other menu font\n  z-index: $zindex-modal; // higher than the sidebars\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: unset;\n  }\n}\n","/* Collapsing of the TOC sidebar while scrolling */\n/* Nav: hide second level (shown on .active) */\n\nnav.page-toc {\n  // A little extra space before the buttons\n  margin-bottom: 1rem;\n}\n\n.bd-toc .nav,\n.list-caption {\n  .nav {\n    display: none;\n\n    // So we can manually specify a level as visible in the config\n    &.visible {\n      display: block;\n    }\n  }\n\n  > .active > ul {\n    display: block;\n  }\n}\n\n// Each entry of the in-page TOC\n.toc-entry {\n  display: block;\n  a > code {\n    color: var(--pst-color-text-muted);\n  }\n\n  a.nav-link {\n    display: block;\n    padding: 0.125rem 0;\n\n    // Padding w/ negative margin so the top TOC item highlight overlaps w/ the TOC border\n    padding-left: 1rem;\n    margin-left: -1rem;\n\n    @include link-sidebar;\n\n    &.active {\n      @include link-sidebar-current;\n      background-color: transparent;\n\n      &:hover {\n        color: var(--pst-color-link-hover);\n      }\n    }\n  }\n}\n","div.versionadded,\ndiv.versionchanged,\ndiv.deprecated {\n  vertical-align: middle;\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0 0.6rem;\n  overflow: hidden;\n  page-break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n  @include box-shadow();\n  position: relative;\n\n  > p {\n    margin-bottom: 0.6rem;\n    margin-top: 0.6rem;\n  }\n}\n\ndiv.versionadded {\n  border-color: var(--pst-color-success);\n  background-color: var(--pst-color-success-bg);\n}\n\ndiv.versionchanged {\n  border-color: var(--pst-color-warning);\n  background-color: var(--pst-color-warning-bg);\n}\n\ndiv.deprecated {\n  border-color: var(--pst-color-danger);\n  background-color: var(--pst-color-danger-bg);\n}\n\nspan.versionmodified {\n  font-weight: 600;\n\n  &:before {\n    margin-right: 0.6rem;\n    color: var(--pst-color-info);\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-versionmodified-default);\n  }\n}\n\nspan.versionmodified.added {\n  &:before {\n    color: var(--pst-color-success);\n    content: var(--pst-icon-versionmodified-added);\n  }\n}\n\nspan.versionmodified.changed {\n  &:before {\n    color: var(--pst-color-warning);\n    content: var(--pst-icon-versionmodified-changed);\n  }\n}\n\nspan.versionmodified.deprecated {\n  &:before {\n    color: var(--pst-color-danger);\n    content: var(--pst-icon-versionmodified-deprecated);\n  }\n}\n",".sidebar-indices-items {\n  display: flex;\n  flex-direction: column;\n  border-top: 1px solid var(--pst-color-border);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    border-top: none;\n  }\n\n  .sidebar-indices-items__title {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    font-size: var(--pst-sidebar-header-font-size);\n    color: var(--pst-color-text-base);\n    margin-bottom: 0.5rem;\n  }\n\n  ul.indices-link {\n    margin-right: -1rem;\n    list-style: none;\n    padding: 0;\n\n    li > a {\n      display: block;\n      padding: 0.25rem 0;\n      color: var(--pst-color-text-muted);\n\n      &:hover {\n        color: var(--pst-color-primary);\n        text-decoration: none;\n        background-color: transparent;\n      }\n    }\n  }\n}\n",".bd-sidebar-primary div#rtd-footer-container {\n  position: sticky;\n  bottom: -1rem;\n  margin: -1rem; // ignore sidebar padding\n\n  .rst-versions.rst-badge {\n    position: unset;\n    font-size: 0.9em;\n    font-family: var(--pst-font-family-base);\n    max-width: unset;\n\n    .rst-current-version {\n      display: flex;\n      align-items: center;\n      gap: 0.2rem;\n      height: 2.5rem;\n      transition: background-color 0.2s ease-out;\n      background-color: var(--pst-color-background);\n      color: var(--pst-color-success);\n      border-top: 1px solid var(--pst-color-border);\n    }\n\n    .fa.fa-book {\n      color: var(--pst-color-text-muted);\n      margin-right: auto;\n\n      &::after {\n        color: var(--pst-color-text-base);\n        content: \"Read The Docs\";\n        font-family: var(--pst-font-family-base);\n        font-weight: var(--pst-admonition-font-weight-heading);\n      }\n    }\n    .fa.fa-caret-down {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  .rst-versions.rst-badge.shift-up {\n    .rst-current-version {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n  }\n\n  .rst-other-versions {\n    background-color: var(--pst-color-surface);\n    color: var(--pst-color-text-base);\n\n    dl {\n      dd a {\n        color: var(--pst-color-text-muted);\n      }\n    }\n\n    hr {\n      background-color: var(--pst-color-border);\n    }\n\n    small a {\n      color: var(--pst-color-link);\n    }\n\n    input {\n      padding-left: 0.5rem;\n      border: 1px solid var(--pst-color-border);\n      background-color: var(--pst-color-surface);\n    }\n  }\n}\n","/**\n * Admonitions and blocks of styled content.\n * Admonitions CSS originally inspired by https://squidfunk.github.io/mkdocs-material/getting-started/\n */\n$admonition-border-radius: 0.25rem;\ndiv.admonition,\n.admonition {\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0.8rem 0.6rem;\n  overflow: hidden;\n  page-break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n  @include box-shadow();\n\n  // Last item should have no spacing since we'll control that w/ padding\n  *:last-child {\n    margin-bottom: 0;\n  }\n\n  // Items after the title should be indented\n  p.admonition-title ~ * {\n    margin-left: 1.4rem;\n    margin-right: 1.4rem;\n  }\n\n  // Lists need to have left margin so they don't spill into it\n  > ol,\n  > ul {\n    margin-left: 1em;\n  }\n\n  // Defaults for all admonitions\n  > .admonition-title {\n    margin: 0 -0.6rem;\n    padding: 0.4rem 0.6rem 0.4rem 2rem;\n    font-weight: var(--pst-admonition-font-weight-heading);\n    position: relative;\n    @include legacy-backdrop-placeholder;\n    background-color: var(--pst-color-info-bg);\n    // now that we use solid colors we want the title on top\n    z-index: 1;\n\n    &:after {\n      position: absolute;\n      left: 0.5rem;\n      width: 1rem;\n      height: 1rem;\n      color: var(--pst-color-info);\n      font: var(--fa-font-solid);\n      line-height: inherit;\n      content: var(--pst-icon-admonition-default);\n      opacity: 1;\n    }\n\n    // Next element after title needs some extra upper-space\n    + * {\n      margin-top: 0.4em;\n    }\n  }\n\n  &.attention {\n    border-color: var(--pst-color-attention);\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &:after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-attention);\n      }\n    }\n  }\n\n  &.caution {\n    border-color: var(--pst-color-warning);\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &:after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-caution);\n      }\n    }\n  }\n\n  &.warning {\n    border-color: var(--pst-color-warning);\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &:after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-warning);\n      }\n    }\n  }\n\n  &.danger {\n    border-color: var(--pst-color-danger);\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &:after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-danger);\n      }\n    }\n  }\n\n  &.error {\n    border-color: var(--pst-color-danger);\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &:after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-error);\n      }\n    }\n  }\n\n  &.hint {\n    border-color: var(--pst-color-success);\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &:after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-hint);\n      }\n    }\n  }\n\n  &.tip {\n    border-color: var(--pst-color-success);\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &:after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-tip);\n      }\n    }\n  }\n\n  &.important {\n    border-color: var(--pst-color-attention);\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &:after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-important);\n      }\n    }\n  }\n\n  &.note {\n    border-color: var(--pst-color-info);\n    > .admonition-title {\n      background-color: var(--pst-color-info-bg);\n\n      &:after {\n        color: var(--pst-color-info);\n        content: var(--pst-icon-admonition-note);\n      }\n    }\n  }\n\n  &.seealso {\n    border-color: var(--pst-color-success);\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &:after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-seealso);\n      }\n    }\n  }\n\n  &.admonition-todo {\n    border-color: var(--pst-color-secondary);\n    > .admonition-title {\n      background-color: var(--pst-color-secondary-bg);\n\n      &:after {\n        color: var(--pst-color-secondary);\n        content: var(--pst-icon-admonition-todo);\n      }\n    }\n  }\n\n  /**\n   * Special-case for a `sidebar` class that makes the admonition float to\n   * the right like the {sidebar} directive.\n   */\n  &.sidebar {\n    max-width: 40%;\n    float: right;\n    clear: both;\n    margin-left: 0.5rem;\n    margin-top: 0;\n\n    // Undo the .sidebar directive border\n    border-width: 0 0 0 0.2rem;\n\n    // TODO: these semantic-color-names border-color rules might no longer be\n    //       needed when we drop support for Sphinx 4 / docutils 0.17\n    &.attention,\n    &.important {\n      border-color: var(--pst-color-attention);\n    }\n\n    &.caution,\n    &.warning {\n      border-color: var(--pst-color-warning);\n    }\n\n    &.danger,\n    &.error {\n      border-color: var(--pst-color-danger);\n    }\n\n    &.hint,\n    &.tip,\n    &.seealso {\n      border-color: var(--pst-color-success);\n    }\n\n    &.note,\n    &.todo {\n      border-color: var(--pst-color-info);\n    }\n\n    // No inner margin since we have less horizontal space w/ the sidebar\n    p.admonition-title ~ * {\n      margin-left: 0rem;\n      margin-right: 0rem;\n    }\n  }\n}\n\n/**************************************************************\n * Similar content blocks that are not technically admonitions.\n */\n\n/**\n * Topics and the {contents} directive\n */\n// Docutils <= 0.17\ndiv.topic,\ndiv.topic.contents,\n// Docutils >= 0.18\nnav.contents,\naside.topic {\n  display: flex;\n  flex-direction: column;\n  background-color: var(--pst-color-surface);\n  border-color: var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n  padding: 1rem 1.25rem;\n  @include box-shadow();\n\n  .topic-title {\n    margin: 0 0 0.5rem 0;\n  }\n\n  // Over-ride text color to ensure enough contrast\n  p {\n    color: var(--pst-color-on-surface) !important;\n  }\n\n  // Over-ride large default padding\n  ul.simple {\n    padding-left: 1rem;\n\n    ul {\n      // So that sub-lists will have a bit less padding\n      padding-left: 2em;\n    }\n  }\n}\n\n/**\n * Sidebar directive\n */\naside.sidebar {\n  border: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-surface);\n  border-radius: $admonition-border-radius;\n  // to match the admonition-styled sidebars:\n  margin-left: 0.5rem;\n  padding: 0;\n  > *:last-child {\n    padding-bottom: 1rem;\n  }\n\n  p.sidebar-title {\n    position: relative;\n    margin-bottom: 0;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n    border-bottom: 1px solid var(--pst-color-border);\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-admonition-font-weight-heading);\n  }\n\n  // Add margin to the first non-`.sidebar-title` item\n  > *:not(.sidebar-title):first-child,\n  > p.sidebar-title + * {\n    margin-top: 1rem;\n  }\n\n  > * {\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n}\n\n/**\n * Rubrics look kind of like section headers\n */\np.rubric {\n  display: flex;\n  flex-direction: column;\n}\n\n/**\n * Seealso is kind of like a vertically-collapsed admonition\n */\n.seealso dd {\n  margin-top: 0;\n  margin-bottom: 0;\n}\n","/**\n * Miscellaneous color functions and mixins\n**/\n\n// loading the math module\n@use \"sass:math\";\n\n// We must add ::before pseudo-element to some theme components (such as admonitions)\n// because users were instructed to customize the background color this way.\n@mixin legacy-backdrop-placeholder {\n  &:before {\n    content: \"\";\n    width: 100%;\n    height: 100%;\n    position: absolute;\n    left: 0;\n    top: 0;\n    z-index: -1;\n    // So that hovering over the text within background is still possible.\n    // Otherwise the background overlays the text and you cannot click or select easily.\n    // ref: https://developer.mozilla.org/en-US/docs/Web/CSS/pointer-events\n    pointer-events: none;\n  }\n}\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetc\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map-get($map, $key);\n  }\n  @return $map;\n}\n\n/**\n  * Function to check if the color needs converting to a \"color\" type\n  * if it is a string we cannot use other color manipulation functions\n  * It is used to create the sphinx-design colours as these are often interpolated\n  */\n// @param {String/Color} $color - Color definition from map\n// @return {Color} - Color type (in hex)\n@function check-color($color) {\n  @if type-of($color) == string {\n    $color: from-hex($color);\n  }\n  @return $color;\n}\n\n/**\n  * Function to convert the string representation of a color to a color type (hex)\n  */\n// @param {String} $string - String representation of a color\n@function from-hex($string) {\n  $string-lower: to-lower-case($string);\n  $r: \"\";\n  $g: \"\";\n  $b: \"\";\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $length: str-length($string);\n  $max: if($length == 4, 1, 2);\n\n  // Check for length accuracy\n  @if $length != 4 and $length != 7 {\n    @return $string;\n  }\n\n  // Loop from the second character (omitting #)\n  @for $i from 2 through $length {\n    $c: str-slice($string-lower, $i, $i);\n\n    // If wrong character, return\n    @if index($hex, $c) == null {\n      @return $string;\n    }\n\n    @if str-length($r) < $max {\n      $r: $r + $c;\n    } @else if str-length($g) < $max {\n      $g: $g + $c;\n    } @else if str-length($b) < $max {\n      $b: $b + $c;\n    }\n  }\n\n  @if $length == 4 {\n    $r: $r + $r;\n    $g: $g + $g;\n    $b: $b + $b;\n  }\n\n  @return rgb(_hex-to-dec($r), _hex-to-dec($g), _hex-to-dec($b));\n}\n\n@function _hex-to-dec($string) {\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $string: to-lower-case($string);\n  $length: str-length($string);\n\n  $dec: 0;\n  @for $i from 1 through $length {\n    $factor: 1 + (15 * ($length - $i));\n    $index: index($hex, str-slice($string, $i, $i));\n    $dec: $dec + $factor * ($index - 1);\n  }\n\n  @return $dec;\n}\n","// Style API docs from sphinx' autodoc / autosummary\n\n/*******************************************************************************\n* Styling for field lists\n*/\n\n/* grey highlighting of 'parameter' and 'returns' field */\ntable.field-list {\n  border-collapse: separate;\n  border-spacing: 10px;\n  margin-left: 1px;\n\n  th.field-name {\n    padding: 1px 8px 1px 5px;\n    white-space: nowrap;\n    background-color: var(--pst-color-surface);\n  }\n\n  /* italic font for parameter types */\n  td.field-body {\n    p {\n      font-style: italic;\n\n      > strong {\n        font-style: normal;\n      }\n    }\n\n    /* reduced space around parameter description */\n    blockquote {\n      border-left: none;\n      margin: 0em 0em 0.3em;\n      padding-left: 30px;\n    }\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary tables\n*/\n\n.table.autosummary {\n  // The first column (with the signature) should not wrap\n  td:first-child {\n    white-space: nowrap;\n  }\n}\n\n/* overriding basic.css to use our own monospace font */\n.sig {\n  font-family: var(--pst-font-family-monospace);\n}\n\n/* C++ specific styling - overriding the basic.css to avoid custom colors*/\n\n.sig-inline.c-texpr,\n.sig-inline.cpp-texpr {\n  font-family: unset;\n}\n\n.sig.c .k,\n.sig.c .kt,\n.sig.cpp .k,\n.sig.cpp .kt {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .m,\n.sig.cpp .m {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .s,\n.sig.c .sc,\n.sig.cpp .s,\n.sig.cpp .sc {\n  color: var(--pst-color-text-base);\n}\n\n// addition\n\n// .sig.c .sig-name .n,\n// .sig.cpp .sig-name .n {\n//   color: var(--pst-color-inline-code);\n// }\n\n.sig-name {\n  color: var(--pst-color-inline-code);\n}\n\n.sig-param .o,\n.sig-param .default_value {\n  color: var(--pst-color-text-muted);\n  font-weight: normal;\n}\n\n// change target color for dark theme\ndt:target,\nspan.highlighted {\n  background-color: var(--pst-color-target);\n}\n\n.viewcode-back {\n  font-family: var(--pst-font-family-base);\n}\n\n.viewcode-block:target {\n  border-top: 1px solid var(--pst-color-border);\n  border-bottom: 1px solid var(--pst-color-border);\n  position: relative;\n  background-color: var(--pst-color-target);\n}\n\n/*******************************************************************************\n* Styling for autosummary titles like \"parameters\" and \"returns\"\n*/\n\n// the API selector\n// from https://github.com/pradyunsg/furo/blob/main/src/furo/assets/styles/content/_api.sass#L6)\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) {\n  //increase margin bottom after the dl elements\n  margin-bottom: 3rem;\n\n  dd {\n    margin-left: 2rem;\n\n    // Fix until this will be solved to Sphinx https://github.com/sphinx-doc/sphinx/issues/10815\n    & > dl.simple > dt {\n      display: flex;\n    }\n  }\n\n  dl.field-list {\n    display: grid;\n    grid-template-columns: unset;\n  }\n\n  dt.field-odd,\n  dt.field-even {\n    margin-top: 0.2rem;\n    margin-bottom: 0.1rem;\n    background-color: var(--pst-color-surface);\n  }\n}\n","/**\n * Code block styling\n * Note that we inherit a lot of styling from Bootstrap so not many rules here.\n */\n\n// General code block behavior\n// Unset bootstrap behavior\ndiv[class*=\"highlight-\"],\ndiv.highlight,\ndiv.literal-block-wrapper {\n  display: flex;\n  flex-direction: column;\n  width: unset;\n  border-radius: $admonition-border-radius;\n}\n\n// Code blocks with captions\n// There's a wrapper when the code block has a title\ndiv.literal-block-wrapper {\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  // This is where the title goes\n  div.code-block-caption {\n    margin: 0;\n    border-bottom: 1px solid var(--pst-color-border);\n    padding: 0.5rem;\n    font-size: 1rem;\n    font-weight: var(--pst-font-weight-caption);\n\n    a.headerlink {\n      font-size: inherit;\n    }\n  }\n\n  // Remove the upper border radius since we want it to connect with the title\n  // Remove the box shadow so the wrapper gets the shadow instead\n  div[class*=\"highlight-\"] {\n    margin: 0;\n    border-radius: 0;\n    pre {\n      border: none;\n      box-shadow: none;\n    }\n  }\n}\n\n/**\n * In-line code\n */\ncode.literal {\n  padding: 0.1rem 0.25rem;\n  background-color: var(--pst-color-surface);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\n\na > code {\n  color: var(--pst-color-inline-code-links);\n}\n\n// Fix for Sphinx's \"highlight\" directive - this is an issue with our accessible pygments theme\n// and the colour we are using for the background of the code blocks.\nhtml[data-theme=\"light\"] .highlight .nf {\n  color: #0078a1 !important;\n}\n\n// Minimum opacity needed for linenos to be WCAG AA conformant\nspan.linenos {\n  opacity: 0.8 !important;\n}\n","figure {\n  a.headerlink {\n    // So that header link doesn't push caption to be off-center.\n    position: absolute;\n    font-size: inherit;\n  }\n  // Default headerlink hover doesn't trigger on figures\n  &:hover a.headerlink {\n    visibility: visible;\n  }\n\n  figcaption {\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-font-weight-caption);\n    color: var(--pst-color-text-muted);\n    margin-left: auto;\n    margin-right: auto;\n\n    table.table {\n      width: fit-content;\n      margin-left: auto;\n      margin-right: auto;\n    }\n  }\n}\n","// For consistency, add bracket around footnotes/citations which are\n// cited more than once. E.g. [Newton](1,2) instead of Newton(1,2)\ndt.label > span.brackets:not(:only-child):before {\n  content: \"[\";\n}\n\ndt.label > span.brackets:not(:only-child):after {\n  content: \"]\";\n}\n\n// Make footnote as a superscript\na.footnote-reference {\n  vertical-align: super;\n  font-size: small;\n}\n\n// Docutils 0.18 uses an `aside.footnote` container with different internal structure\naside.footnote {\n  margin-bottom: 0.5rem;\n  &:last-child {\n    margin-bottom: 1rem;\n  }\n  span.label,\n  span.backrefs {\n    font-weight: bold;\n  }\n\n  &:target {\n    background-color: var(--pst-color-target);\n  }\n}\n","/**\n * Hacky fixes that don't fit cleanly into other sections\n */\n\n// Ensure user highlighting/selecting behaves properly\n// From https://stackoverflow.com/a/34372191\ntable.highlighttable td.linenos,\nspan.linenos,\ndiv.doctest > div.highlight span.gp {\n  /* gp: Generic.Prompt */\n  user-select: none;\n  -webkit-user-select: text; /* Safari fallback only */\n  -webkit-user-select: none; /* Chrome/Safari */\n  -moz-user-select: none; /* Firefox */\n  -ms-user-select: none; /* IE10+ */\n}\n","// Override bootstrap by restoring the basic theme default.\ndd {\n  margin-top: 3px;\n  margin-bottom: 10px;\n  margin-left: 30px;\n}\n\nol,\nul {\n  padding-inline-start: 2rem;\n  li > p:first-child {\n    margin-bottom: 0.25rem;\n    margin-top: 0.25rem;\n  }\n}\n","// GitHub blockquote style\nblockquote {\n  padding: 1em 1em;\n  color: var(--pst-color-text-muted);\n  border-left: 0.25em solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n  position: relative;\n\n  p {\n    color: var(--pst-color-text-base);\n  }\n\n  // remove padding from included line-block to avoid duplication\n  .line-block {\n    margin: 0 0;\n  }\n\n  // remove margin bottom for the last p\n  p:last-child {\n    margin-bottom: 0;\n  }\n\n  @include legacy-backdrop-placeholder;\n  background-color: var(--pst-color-surface);\n\n  //hack to make the text in the blockquote selectable\n  &:before {\n    z-index: -1;\n  }\n}\n","/**\n * Span-level styling within content\n */\n\nspan.guilabel {\n  border: 1px solid var(--pst-color-info);\n  color: var(--pst-color-info);\n  font-size: 80%;\n  font-weight: 700;\n  border-radius: 4px;\n  padding: 2.4px 6px;\n  margin: auto 2px;\n  position: relative;\n  @include legacy-backdrop-placeholder;\n  background-color: var(--pst-color-info-bg);\n}\n\na.reference.download:before {\n  content: var(--pst-icon-download);\n  font: var(--fa-font-solid);\n  font-size: 0.8em;\n  padding: 0 0.25em;\n  color: var(--pst-color-text-muted);\n}\n","/**\n * Tables\n */\n\n// ensure table will fit in the article width and make them y-scrollable\ntable {\n  display: table;\n  overflow: auto;\n\n  // default to table-center\n  margin-left: auto;\n  margin-right: auto;\n\n  &.table-right {\n    margin-right: 0;\n  }\n\n  &.table-left {\n    margin-left: 0;\n  }\n}\n\n// customize table caption from bootstrap\n// to display them on top and centered\ntable caption {\n  text-align: center;\n  caption-side: top;\n  color: var(--pst-color-text-muted);\n}\n\n// MyST Markdown tables use these classes to control alignment\nth,\ntd {\n  &.text-align\\:left {\n    text-align: left;\n  }\n\n  &.text-align\\:right {\n    text-align: right;\n  }\n\n  &.text-align\\:center {\n    text-align: center;\n  }\n}\n\n// override bootstrap background colors\n.table {\n  --bs-table-bg: transparent;\n}\n","/**\n * Style the toctree component in pages (avoid modifying the navbars)\n */\n.toctree-wrapper {\n  p.caption {\n    font-size: 1.5em;\n    margin-bottom: 0em;\n  }\n\n  & > ul {\n    padding-left: 0;\n  }\n\n  li[class^=\"toctree-l\"] {\n    list-style: none;\n    margin-bottom: 0.2em;\n\n    & > a {\n      list-style: none;\n      font-size: 1.1em;\n    }\n\n    & > ul {\n      list-style: none;\n      padding-inline-start: 1.5em;\n    }\n  }\n\n  // slightly bigger font for l1\n  .toctree-l1 > a {\n    font-size: 1.3em;\n  }\n}\n\ndiv.topic.contents,  // Docutils <= 0.17\nnav.contents  // Docutils >= 0.18\n{\n  // Style similarly to toctree\n  ul.simple {\n    list-style: none;\n    padding-left: 0;\n  }\n}\n","/**\n * Mathematics via MathJax.\n *\n * This is designed for MathJax v3\n * ref: https://www.sphinx-doc.org/en/master/usage/extensions/math.html#module-sphinx.ext.mathjax\n */\n\n// Applies to all math elements\nspan.math,\ndiv.math {\n  align-items: center;\n  display: flex;\n  max-width: 100%;\n  // This will be over-ridden for the y-direction and divs\n  overflow: hidden;\n}\n\n// Inline-only\nspan.math {\n  display: inline-flex;\n}\n\n// Block-level only\ndiv.math {\n  gap: 0.5em;\n  // So that the eqno shows up after the equation\n  flex-direction: row-reverse;\n\n  // The equation number / link\n  span.eqno a.headerlink {\n    position: relative;\n    font-size: 1em;\n  }\n\n  // The math container\n  mjx-container {\n    flex-grow: 1;\n    padding-bottom: 0.2rem;\n    overflow: auto;\n\n    // Set height to 0 so that it does not cause scrollbars to appear\n    // ref: https://github.com/mathjax/MathJax/issues/2521\n    mjx-assistive-mml {\n      height: 0;\n    }\n  }\n}\n","/**\n * ABlog\n * ref: https://ablog.readthedocs.io/\n */\n\n/**\n * Sidebar template components\n */\n.ablog-sidebar-item {\n  h2,\n  h3 {\n    font-size: var(--pst-sidebar-header-font-size);\n    // Remove unnecessary vertical whitespace\n    margin-top: 0.5rem;\n\n    // The headers are all links, but this makes them hard to parse\n    // So we change the colors to make them look like headers\n    a {\n      color: var(--pst-color-text-base);\n    }\n  }\n\n  ul {\n    // No bullet points for the primary sidebar items\n    list-style: none;\n    padding-left: 0;\n    // Otherwise a scrollbar randomly shows up\n    overflow-y: hidden;\n    // List of recent post items\n    display: flex;\n    flex-direction: column;\n    gap: 0.5em;\n    margin-bottom: 0;\n\n    // The ablog cloud should move horizontally\n    &.ablog-cloud {\n      flex-direction: row;\n      flex-flow: wrap;\n      gap: 0.5rem;\n\n      // Vertical-align tag clouds\n      li {\n        // Center the tag cloud items\n        display: flex;\n        align-items: center;\n      }\n    }\n  }\n}\n\n/**\n * Previous / next buttons at the bottom\n */\n.ablog__prev-next {\n  font-size: 1.2em;\n  display: flex;\n  padding: 1rem 0;\n\n  // The bottom previous / next arrows\n  > span {\n    // To ensure that the whole thing fits on one line even if there are long titles\n    display: flex;\n    max-width: 45%;\n\n    // Links within each span have the collection of icon + text\n    a {\n      display: flex;\n      align-items: center;\n      margin-left: auto;\n      gap: 1rem;\n      line-height: 1.5rem;\n\n      i:before {\n        color: var(--pst-color-text-base);\n      }\n    }\n  }\n\n  // The first span is for the previous page and aligns to the left\n  span.ablog__prev {\n    i.fa-arrow-circle-left:before {\n      content: var(--pst-icon-angle-left);\n    }\n  }\n\n  // The second span is just an empty space so we remove it because we're\n  // positioning with flex\n  span.ablog__spacer {\n    display: none;\n  }\n\n  // The third span is aligned to the right\n  span.ablog__next {\n    margin-left: auto;\n    text-align: right;\n\n    i.fa-arrow-circle-right:before {\n      content: var(--pst-icon-angle-right);\n    }\n  }\n}\n\n/**\n * {postlist} directive and posts page\n */\n.ablog__collection,\n.postlist {\n  padding-left: 0;\n\n  .ablog-post {\n    list-style: none;\n\n    // Post metadata tags (author, links ,etc) should be a bit smaller\n    .ablog-archive {\n      display: flex;\n      flex-direction: row;\n      flex-wrap: wrap;\n      gap: 1rem;\n      list-style: none;\n      font-size: 0.75rem;\n      padding-left: 0;\n    }\n\n    // Title line should be a bit bigger and bold to stand out\n    .ablog-post-title {\n      margin-top: 0;\n      font-size: 1.25rem;\n\n      a {\n        font-weight: bold;\n      }\n    }\n\n    // Read more button should be a bit bigger\n    .ablog-post-expand {\n      margin-bottom: 0.5rem;\n    }\n  }\n}\n","/**\n * Special cases for Bootstrap functionality\n */\n\n// Bootstrap adds margin to their general container class. However, sphinx/docutils\n// can also generate output with the container class, but in those cases we should\n// not add the margin from bootstrap. Same for max-width.\n.docutils.container {\n  padding-left: unset;\n  padding-right: unset;\n  margin-left: unset;\n  margin-right: unset;\n  max-width: unset;\n  width: unset;\n}\n","/**\n * Sphinx Copybutton\n * ref: https://sphinx-copybutton.readthedocs.io/\n */\n\ndiv.highlight button.copybtn {\n  // Nicer spacing\n  display: flex;\n  align-items: center;\n  justify-content: center;\n\n  // Removes the button background so we more naturally blend with the code cell.\n  background-color: unset;\n\n  // Don't over-ride the success color\n  &:not(.success) {\n    color: var(--pst-color-muted);\n  }\n  border: none;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    &:not(.success) {\n      color: var(--pst-color-text);\n      background-color: var(--pst-color-shadow);\n    }\n  }\n\n  // Tooltip styling\n  &.o-tooltip--left:after {\n    color: var(--pst-color-text);\n    background-color: var(--pst-color-surface);\n  }\n}\n","// adapt ethical ad to the theme\n#ethical-ad-placement {\n  .ethical-sidebar a,\n  .ethical-sidebar a:visited,\n  .ethical-sidebar a:hover,\n  .ethical-sidebar a:active,\n  .ethical-footer a,\n  .ethical-footer a:visited,\n  .ethical-footer a:hover,\n  .ethical-footer a:active {\n    color: var(--pst-color-text-base);\n  }\n\n  .ethical-sidebar,\n  .ethical-footer {\n    background-color: var(--pst-color-background);\n    border: 1px solid var(--pst-color-border);\n    border-radius: 5px;\n    color: var(--pst-color-text-base);\n    font-size: 14px;\n    line-height: 20px;\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/******************************************************************************\n * Jupyter Sphinx\n */\n\n.bd-content div.jupyter_container {\n  // We don't want borders around the whole container, just around code cells\n  border: none;\n  background-color: unset;\n  box-shadow: none;\n\n  // Code cells should have the same style as our other code objects\n  div.output,\n  div.highlight {\n    border-radius: 0.25rem;\n  }\n  div.highlight {\n    background-color: var(--pst-color-surface);\n  }\n\n  // Ensure the style is the same as our code cells. Jupyter Sphinx makes it tiny.\n  .cell_input,\n  .cell_output {\n    border-radius: 0.25rem;\n    pre {\n      padding: 1rem;\n    }\n  }\n}\n","/**\n * Special-cases for packages in the PyData ecosystem\n */\n\n// xarray output display in bootstrap\n.xr-wrap[hidden] {\n  display: block !important;\n}\n","/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetch\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map-get($map, $key);\n  }\n  @return $map;\n}\n\n/* Assign base colors for the PyData theme */\n$color-palette: (\n  // Primary color\n  \"teal\":\n    (\n      \"50\": #f4fbfc,\n      \"100\": #e9f6f8,\n      \"200\": #d0ecf1,\n      \"300\": #abdde6,\n      \"400\": #3fb1c5,\n      \"500\": #0a7d91,\n      \"600\": #085d6c,\n      \"700\": #064752,\n      \"800\": #042c33,\n      \"900\": #021b1f,\n    ),\n  // Secondary color\n  \"violet\":\n    (\n      \"50\": #f4eefb,\n      \"100\": #e0c7ff,\n      \"200\": #d5b4fd,\n      \"300\": #b780ff,\n      \"400\": #9c5ffd,\n      \"500\": #8045e5,\n      \"600\": #6432bd,\n      \"700\": #4b258f,\n      \"800\": #341a61,\n      \"900\": #1e0e39,\n    ),\n  // Neutrals\n  \"gray\":\n    (\n      \"50\": #f9f9fa,\n      \"100\": #f3f4f5,\n      \"200\": #e5e7ea,\n      \"300\": #d1d5da,\n      \"400\": #9ca4af,\n      \"500\": #677384,\n      \"600\": #48566b,\n      \"700\": #29313d,\n      \"800\": #222832,\n      \"900\": #14181e,\n    ),\n  // Accent color\n  \"pink\":\n    (\n      \"50\": #fcf8fd,\n      \"100\": #fcf0fa,\n      \"200\": #f8dff5,\n      \"300\": #f3c7ee,\n      \"400\": #e47fd7,\n      \"500\": #c132af,\n      \"600\": #912583,\n      \"700\": #6e1c64,\n      \"800\": #46123f,\n      \"900\": #2b0b27,\n    ),\n  \"foundation\": (\n    \"white\": #ffffff,\n    // gray-900\n    \"black\": #14181e,\n  )\n);\n\n:root {\n  // Add theme colours to the html root element\n  @each $group-color, $color in $color-palette {\n    @each $color-name, $definition in $color {\n      --pst-#{$group-color}-#{$color-name}: #{$definition};\n    }\n  }\n}\n\n// Static SCSS variables used thoroughout the theme\n// Minimum contrast ratio used for the theme.\n// Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n// 4.5 - is for text that is 14pt or less\n$min-contrast-ratio-4: 4.5;\n// 3 is for text that is 18pt or bold, or for non-text elements\n$min-contrast-ratio-3: 3;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$foundation-black: #14181e;\n$foundation-white: #ffffff;\n// This is a custom - calculated  color between gray 100 and 200 - to reduce\n// the contrast ratio (avoid a jarring effect)\n$base-light-text: #ced6dd;\n// used in sphinx_design - gray 100\n$foundation-light-gray: #f3f4f5;\n// used in sphinx_design - gray 700\n$foundation-muted-gray: #29313d;\n// used in sphinx_design - gray 800\n$foundation-dark-gray: #222832;\n\n$pst-semantic-colors: (\n  \"primary\": (\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"teal\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"teal\", \"800\")},\n  ),\n  \"secondary\": (\n    \"light\": #{map-deep-get($color-palette, \"violet\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"violet\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"800\")},\n  ),\n  \"accent\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"pink\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"pink\", \"800\")},\n  ),\n  \"info\": (\n    \"light\": #276be9,\n    \"bg-light\": #dce7fc,\n    \"dark\": #79a3f2,\n    \"bg-dark\": #06245d,\n  ),\n  \"warning\": (\n    \"light\": #f66a0a,\n    \"bg-light\": #f8e3d0,\n    \"dark\": #ff9245,\n    \"bg-dark\": #652a02,\n  ),\n  \"success\": (\n    \"light\": #00843f,\n    \"bg-light\": #d6ece1,\n    \"dark\": #5fb488,\n    \"bg-dark\": #002f17,\n  ),\n  // This is is based on the warning color\n  \"attention\":\n    (\n      \"light\": var(--pst-color-warning),\n      \"bg-light\": var(--pst-color-warning-bg),\n      \"dark\": var(--pst-color-warning),\n      \"bg-dark\": var(--pst-color-warning-bg),\n    ),\n  \"danger\": (\n    \"light\": #d72d47,\n    \"bg-light\": #f9e1e4,\n    \"dark\": #e78894,\n    \"bg-dark\": #4e111b,\n  ),\n  \"text-base\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    \"dark\": $base-light-text,\n  ),\n  \"text-muted\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"heading-color\": (\n    \"light\": #{$foundation-white},\n    \"dark\": #{$foundation-black},\n  ),\n  \"shadow\": (\n    \"light\": rgba(0, 0, 0, 0.1),\n    \"dark\": rgba(0, 0, 0, 0.2),\n  ),\n  \"border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"300\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n  ),\n  \"border-muted\": (\n    \"light\": rgba(23, 23, 26, 0.2),\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  \"inline-code\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"300\")},\n  ),\n  \"inline-code-links\": (\n    // need to make sure there is enough contrast against the code bg\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"600\")},\n    // keep primary color for dark mode\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n  ),\n  \"target\": (\n    \"light\": #f3cf95,\n    \"dark\": #675c04,\n  ),\n  // DEPTH COLORS - you can see the elevation colours and shades\n  // in the Figma file https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1492%3A922&t=sQeQZehkOzposYEg-1\n  // background: color of the canvas / the furthest back layer\n  \"background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    ),\n  // on-background: provides slight contrast against background\n  // (by use of shadows in light theme)\n  \"on-background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    ),\n  \"surface\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  // on_surface: object on top of surface object (without shadows)\n  \"on-surface\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n      \"dark\": $foundation-light-gray,\n    ),\n);\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * #{something} inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n@mixin theme-colors($mode) {\n  // check if this color is defined differently for light/dark\n  @each $col-name, $definition in $pst-semantic-colors {\n    @if type-of($definition) == map {\n      @each $key, $val in $definition {\n        @if str-index($key, $mode) != null {\n          // since now we define the bg colours in the semantic colours and not\n          // by changing opacity, we need to check if the key contains bg and the\n          // correct mode (light/dark)\n          @if str-index($key, \"bg\") != null {\n            --pst-color-#{$col-name}-bg: #{$val};\n          } @else {\n            --pst-color-#{$col-name}: #{$val};\n          }\n        }\n      }\n    } @else {\n      --pst-color-#{$col-name}: #{$definition};\n    }\n  }\n\n  // assign the \"duplicate\" colors (ones that just reference other variables)\n  & {\n    --pst-color-link: var(--pst-color-primary);\n    --pst-color-link-hover: var(--pst-color-secondary);\n  }\n  // adapt to light/dark-specific content\n  @if $mode == \"light\" {\n    .only-dark,\n    .only-dark ~ figcaption {\n      display: none !important;\n    }\n  } @else {\n    .only-light,\n    .only-light ~ figcaption {\n      display: none !important;\n    }\n    /* Adjust images in dark mode (unless they have class .only-dark or\n     * .dark-light, in which case assume they're already optimized for dark\n     * mode).\n     */\n    img:not(.only-dark):not(.dark-light) {\n      filter: brightness(0.8) contrast(1.2);\n    }\n    /* Give images a light background in dark mode in case they have\n    *  transparency and black text (unless they have class .only-dark or .dark-light, in\n    *  which case assume they're already optimized for dark mode).\n    */\n    .bd-content img:not(.only-dark):not(.dark-light) {\n      background: rgb(255, 255, 255);\n      border-radius: 0.25rem;\n    }\n    // MathJax SVG outputs should be filled to same color as text.\n    .MathJax_SVG * {\n      fill: var(--pst-color-text-base);\n    }\n  }\n}\n\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  @include theme-colors(\"light\");\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    @include theme-colors($mode);\n    color-scheme: $mode;\n  }\n}\n\n// assign classes too, for runtime use of theme colors\n@each $col-name, $definition in $pst-semantic-colors {\n  .pst-color-#{$col-name} {\n    color: var(--pst-color-#{$col-name});\n  }\n}\n","/*******************************************************************************\n * Special-cases for the sphinx-design library, mainly to make it compatible\n * with the dark/light themes of pydata-sphinx-theme.\n *\n * NOTE: sphinx-design uses !important quite liberally, so here we must do the\n * same for our overrides to have any effect.\n */\n@use \"../variables/color\" as pst-color;\n@use \"sass:meta\";\n@use \"sass:color\";\n\n/*******************************************************************************\n  * Color and variables\n  *\n  * This is a list of the semantic color names from sphinx-design (we only\n  * need to override variables that sphinx-design has actually defined).\n  * https://github.com/executablebooks/sphinx-design/blob/9226a12a/style/_colors.scss#L31-L43\n  */\n$sd-semantic-color-names: (\n  \"primary\",\n  \"secondary\",\n  \"success\",\n  \"info\",\n  \"warning\",\n  \"danger\",\n  \"light\",\n  \"muted\",\n  \"dark\",\n  \"black\",\n  \"white\"\n);\n\n/**\n  * Here we create some extra --pst-color-* variables and use\n  * them to override the value of the corresponding sphinx-design variables.\n  * This is easier than re-writing the sphinx-design rules. Even easier would be\n  * directly assigning our values to the --sd-color-* variables, but then our\n  * downstream users couldn't override *our* colors and have it affect buttons\n  * and badges.\n  *\n  * First, define the extra keys needed to cover the full range of semantic\n  * color names used in sphinx-design, then merge them with the names we\n  * already define for our own needs.\n  * see https://sphinx-design.readthedocs.io/en/latest/css_variables.html\n  */\n$extra-semantic-colors: (\n  \"white\": $foundation-white,\n  \"light\": (\n    light: $foundation-light-gray,\n    bg-light: color.scale($foundation-light-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-light-gray, $lightness: -30%),\n  ),\n  \"muted\": (\n    light: $foundation-muted-gray,\n    bg-light: color.scale($foundation-muted-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-muted-gray, $lightness: -30%),\n  ),\n  \"dark\": $foundation-dark-gray,\n  \"black\": $foundation-black,\n);\n\n$all-colors: map-merge($pst-semantic-colors, $extra-semantic-colors);\n\n@mixin create-sd-colors($value, $name) {\n  // define the pst variables, so that downstream user overrides will work\n  --pst-color-#{$name}: #{$value};\n  // we are now using a11y-combination to calculate the text color - this is based\n  // on the WCAG color contrast guidelines\n  --pst-color-#{$name}-text: #{a11y-combination($value)};\n  //TODO: highlight seems to be used for buttons @trallard to fix on a11y follow-up work\n  --pst-color-#{$name}-highlight: #{darken($value, 15%)};\n  // override the sphinx-design variables\n  --sd-color-#{$name}: var(--pst-color-#{$name});\n  --sd-color-#{$name}-text: var(--pst-color-#{$name}-text);\n  //TODO: highlight seems to be used for buttons @trallard to fix on a11y follow-up work\n  --sd-color-#{$name}-highlight: var(--pst-color-#{$name}-highlight);\n}\n\n// Now we override the --sd-color-* variables.\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    // check if this color is defined differently for light/dark\n    @each $name in $sd-semantic-color-names {\n      $definition: map-get($all-colors, $name);\n      @if type-of($definition) == map {\n        @each $key, $value in $definition {\n          @if str-index($key, $mode) != null {\n            // since now we define the bg colours in the semantic colours and not\n            // by changing opacity, we need to check if the key contains bg and the\n            // correct mode (light/dark)\n            @if str-index($key, \"bg\") != null {\n              --sd-color-#{$name}-bg: #{$value};\n              // create local variable\n              $bg-var: --sd-color-#{$name}-bg;\n              $value: check-color($value);\n              --sd-color-#{$name}-bg-text: #{a11y-combination($value)};\n            } @else {\n              $value: check-color($value);\n              @include create-sd-colors($value, $name);\n            }\n          }\n        }\n      } @else {\n        $value: map-get($all-colors, $name);\n        @include create-sd-colors($value, $name);\n      }\n    }\n  }\n}\n\n// Make sure the color border variables are set using our variables\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    --sd-color-card-border: var(--pst-color-border);\n  }\n}\n\n/*******************************************************************************\n  * shadows\n  */\nhtml[data-theme=\"light\"] {\n  .sd-shadow-xs,\n  .sd-shadow-sm,\n  .sd-shadow-md,\n  .sd-shadow-lg {\n    @include box-shadow();\n  }\n}\n\n/*******************************************************************************\n  * cards\n  */\n\n.bd-content .sd-card {\n  border: 1px solid var(--pst-color-border);\n\n  // TODO - --pst-color-panel-background is not defined... where is this coming from?\n  .sd-card-header {\n    background-color: var(--pst-color-panel-background);\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n  .sd-card-footer {\n    background-color: var(--pst-color-panel-background);\n    border-top: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-body {\n    background-color: var(--pst-color-panel-background);\n  }\n}\n/*******************************************************************************\n  * tabs\n  */\n\n.bd-content .sd-tab-set {\n  > input {\n    // Active tab label\n    &:checked + label {\n      border-color: transparent transparent var(--pst-color-primary); // top LR bottom\n      color: var(--pst-color-primary);\n    }\n\n    // Hover label\n    &:not(:checked) + label:hover {\n      border-color: var(--pst-color-secondary);\n      color: var(--pst-color-secondary);\n    }\n  }\n\n  // Tab label\n  > label {\n    color: var(--pst-color-text-muted);\n    border-top: 0.125rem solid transparent; // so hover isn't just color change\n    padding-top: 0.5em; // same as bottom padding, so hover overline looks OK\n    // Hovered label\n    html &:hover {\n      color: var(--pst-color-secondary);\n      border-color: var(--pst-color-secondary);\n    }\n  }\n}\n\n/*******************************************************************************\n* Dropdowns\n*/\n\ndetails.sd-dropdown {\n  // Remove all borders to over-ride SD behavior, and we'll add our own later\n  border: 0px !important;\n  summary.sd-card-header {\n    border: 0 !important;\n    & + div.sd-summary-content {\n      border: 0;\n    }\n  }\n  // Drop shadow should behave same as admonitions\n  @include box-shadow();\n\n  // Header is where the \"clickable\" box goes\n  summary.sd-card-header {\n    display: flex;\n    align-items: center;\n    position: relative; // So background color works\n    font-weight: 600;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n\n    // Set a variable that we can re-use for colors later\n    // We must set this in the current and content sibling container\n    // so that it is defined in both places\n    --pst-sd-dropdown-color: var(--pst-gray-500);\n    --pst-sd-dropdown-bg-color: var(--pst-color-surface);\n    & + div.sd-summary-content {\n      --pst-sd-dropdown-color: var(--sd-color-card-border);\n    }\n    @each $name in $sd-semantic-color-names {\n      &.sd-bg-#{$name} {\n        --pst-sd-dropdown-color: var(--sd-color-#{$name});\n        --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n        // Otherwise it won't be defined in the sibling element\n        & + div.sd-summary-content {\n          --pst-sd-dropdown-color: var(--sd-color-#{$name});\n          --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n        }\n      }\n      &.sd-bg-text-#{$name} {\n        // Use the WCAG conformant text color\n        color: var(--sd-color-#{$name}-bg-text) !important;\n      }\n    }\n\n    @include legacy-backdrop-placeholder;\n    background-color: var(--pst-sd-dropdown-bg-color) !important;\n\n    // Add a left border with the same structure as our admonitions\n    border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n    & + div.sd-summary-content {\n      border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n      border-bottom-left-radius: calc(0.25rem - 1px);\n      background-color: var(--pst-color-on-background);\n    }\n    span.sd-summary-icon {\n      display: inline-flex;\n      align-items: center;\n      color: var(--pst-sd-dropdown-color) !important;\n      svg {\n        opacity: 1;\n      }\n    }\n\n    // Positioning of the caret\n    .sd-summary-up,\n    .sd-summary-down {\n      top: 0.7rem;\n    }\n  }\n}\n","/**\n * Sphinx togglebutton\n */\n\n.bd-content {\n  // Admonition toggles\n  .admonition {\n    button.toggle-button {\n      color: inherit;\n    }\n  }\n\n  // Details buttons\n  details.toggle-details {\n    // Over-ride border color to re-use our primary color\n    summary {\n      border-left: 3px solid var(--pst-color-primary);\n    }\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries to display pre-executed notebooks.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/*******************************************************************************\n * nbsphinx\n */\nhtml div.rendered_html\n// NBsphinx ipywidgets output selector\nhtml .jp-RenderedHTMLCommon {\n  table {\n    table-layout: auto;\n  }\n}\n\n// Dark theme special-cases\nhtml[data-theme=\"dark\"] .bd-content {\n  .nboutput {\n    .output_area.rendered_html {\n      @include cell-output-background;\n    }\n\n    .output_area.stderr {\n      background: var(--pst-color-danger);\n    }\n  }\n}\n\n// Add extra padding to the final item in an nbsphinx container\ndiv.nblast.container {\n  margin-bottom: 1rem;\n}\n\n/*******************************************************************************\n * myst NB\n */\n\ndiv.cell_output .output {\n  max-width: 100%;\n  overflow-x: auto;\n}\n\n// Dark theme special-cases\nhtml[data-theme=\"dark\"] .bd-content {\n  div.cell_output {\n    img,\n    .text_html {\n      @include cell-output-background;\n    }\n  }\n}\n\n// Prevent tables from scrunching together\n.bd-content {\n  div.cell_input {\n    display: flex;\n    flex-direction: column;\n    justify-content: stretch;\n  }\n\n  div.cell_input,\n  div.output {\n    border-radius: $admonition-border-radius;\n  }\n\n  div.output {\n    table {\n      table-layout: auto;\n    }\n  }\n}\n","/**\n * style for the various mapping libs based on leaflet (folium, geemap, ipyleaflet)\n * mainly ensure the good display of the maps in both themes and avoid the customization\n * of the tiles\n */\n\n/**\n * avoid border override from pydata-sphinx-theme\n * minimal selctor to get the priority\n */\nhtml[data-theme=\"dark\"] .bd-content img.leaflet-tile.leaflet-tile-loaded {\n  border-radius: 0;\n  padding: 0;\n}\n","/**\n * /search.html page special-cases\n */\n\n.bd-search-container {\n  /*******************************************\n  * Search results\n  */\n  // Whitespace\n  div#search-results {\n    > h2 {\n      font-size: var(--pst-font-size-icon);\n      margin-top: 0;\n    }\n\n    p.search-summary {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  ul.search {\n    margin: 0;\n    list-style: none;\n\n    li {\n      background-image: none;\n      padding: 1rem 0;\n      margin: 1rem 0;\n      border-top: 1px solid var(--pst-color-text-muted);\n\n      // First link is the page title, it should be a bit bigger\n      > a {\n        font-size: 1.2em;\n      }\n\n      div.context,\n      p.context {\n        color: var(--pst-color-text-base);\n        margin: 0.5em 0 0 0;\n\n        // Add a # before page section titles to make it clear they are sections\n        a:before {\n          content: \"#\";\n          padding-right: 0.2em;\n          color: var(--pst-color-text-muted);\n        }\n      }\n    }\n  }\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/2023.2/_static/styles/sphinx-book-theme.css b/2023.2/_static/styles/sphinx-book-theme.css
new file mode 100644
index 0000000000..e514f24e36
--- /dev/null
+++ b/2023.2/_static/styles/sphinx-book-theme.css
@@ -0,0 +1,8 @@
+/*! sphinx-book-theme CSS
+ * BSD 3-Clause License
+ * Copyright (c) 2020, EBP
+ * All rights reserved.
+ *
+ * This follows the 7-1 pattern described here:
+ * https://sass-guidelin.es/#architecture
+ */html{--pst-font-size-base:none}html[data-theme=light]{--sbt-color-announcement:#616161}html[data-theme=dark],html[data-theme=light]{--pst-color-primary:#579aca}html{scroll-padding-top:4rem}.sbt-scroll-pixel-helper{position:absolute;width:0;height:0;top:0;left:0}.d-n,.onlyprint{display:none}@media print{.onlyprint{display:block!important}}@media print{.noprint{display:none!important}}.bd-article-container h1,.bd-article-container h2,.bd-article-container h3,.bd-article-container h4,.bd-article-container h5,.bd-article-container p.caption{color:var(--pst-color-muted)}.bd-article-container h1,.bd-article-container h2{font-weight:500}a.brackets:before{color:inherit;font-family:inherit;margin-right:0}table{position:relative}@media print{.bd-main .bd-content{margin-left:2rem;height:auto}.bd-main .bd-content #jb-print-docs-body{margin-left:0}.bd-main .bd-content #jb-print-docs-body h1{font-size:3em;text-align:center;margin-bottom:0}.bd-main .bd-content .bd-article{padding-top:0}.bd-main .bd-content .bd-article h1:first-of-type{display:none}.bd-main .bd-content .container{min-width:0!important}.bd-main .bd-content h1{margin-top:1em;margin-bottom:1em}.bd-main .bd-content h1,.bd-main .bd-content h2,.bd-main .bd-content h3,.bd-main .bd-content h4{break-after:avoid;color:#000}.bd-main .bd-content table{break-inside:avoid}.bd-main .bd-content pre{word-wrap:break-word}.bd-main .bd-content a.headerlink{display:none}.bd-main .bd-content aside.margin,.bd-main .bd-content aside.sidebar,.bd-main .bd-content blockquote.epigraph{border:none}.bd-main .bd-content .footer{margin-top:1em}.bd-main .bd-content #jb-print-toc{margin-bottom:1.5rem;margin-left:0}.bd-main .bd-content #jb-print-toc .section-nav{border-left:0!important;list-style-type:disc!important;margin-left:3em!important}.bd-main .bd-content #jb-print-toc .section-nav a{text-decoration:none!important}.bd-main .bd-content #jb-print-toc .section-nav li{display:list-item!important}.bd-main .bd-content #jb-print-toc 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(max-width:768px){label.sidebar-toggle.secondary-toggle{margin-bottom:0}}.bd-header-article{display:flex;align-items:center;position:sticky;top:0;background-color:var(--pst-color-background);transition:left .2s;font-size:.9em;padding:0 1rem;z-index:1020}@media (max-width:768px){.bd-header-article{padding:0 .5rem;height:3.5rem}}.scrolled .bd-header-article{box-shadow:0 6px 6px -6px var(--pst-color-shadow)}.bd-header-article .header-article__inner{padding:0}.bd-header-article .header-article-items{display:flex;align-items:center;height:3rem;width:100%}.bd-header-article .header-article-item{display:flex;align-items:center}.bd-header-article .article-header-buttons{display:flex}.bd-header-article .btn{font-size:1.3rem;color:var(--pst-color-text-muted);border:none;padding:0 .5rem}.bd-header-article .btn.show,.bd-header-article .btn:hover{color:var(--pst-color-text-base);border:none}.bd-header-article .btn.show+.dropdown-menu,.bd-header-article .btn:hover+.dropdown-menu{display:block}.bd-header-article .btn:focus{box-shadow:none}.bd-header-article .btn.dropdown-toggle:after{display:none}.bd-header-article .theme-switch-button{margin:0}.bd-header-article .theme-switch-button button,.bd-header-article .theme-switch-button i,.bd-header-article .theme-switch-button span{padding:0}.bd-header-article .theme-switch-button i,.bd-header-article .theme-switch-button span{transition:color .25s ease-out}.bd-header-article .theme-switch-button:active,.bd-header-article .theme-switch-button:hover{background-color:unset!important}.bd-header-article .theme-switch-button:active i,.bd-header-article .theme-switch-button:active span,.bd-header-article .theme-switch-button:hover i,.bd-header-article .theme-switch-button:hover span{color:var(--pst-color-text-base)}.bd-header-article .dropdown-menu{margin-top:0;transform:translateX(-75%);box-shadow:0 .2rem .5rem var(--pst-color-shadow),0 0 .0625rem 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a{font-size:1rem}}img{max-width:100%}img.align-center{margin-left:auto;margin-right:auto;display:block}img.align-left{clear:left;float:left;margin-right:1em}img.align-right{clear:right;float:right;margin-left:1em}div.figure{width:100%;margin-bottom:1em;text-align:center}div.figure.align-left{text-align:left}div.figure.align-left p.caption{margin-left:0}div.figure.align-right{text-align:right}div.figure.align-right p.caption{margin-right:0}div.figure p.caption{margin:.5em 10%}div.figure.margin-caption p.caption,div.figure.margin p.caption{margin:.5em 0}div.figure.margin-caption p.caption{text-align:left}div.figure span.caption-number{font-weight:700}div.figure span{font-size:.9rem}label.margin-toggle{margin-bottom:0}label.margin-toggle.marginnote-label{display:none}label.margin-toggle sup{user-select:none}@media 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(max-width:992px){span.marginnote,span.sidenote{display:none}}aside.sidebar .note{margin:1rem;padding:0 0 1rem}aside.sidebar .admonition-title{margin:0 -1rem 0 0}aside.sidebar.margin .sidebar-title:empty{display:none}aside.sidebar.margin .admonition{margin:.5rem;padding-left:0;padding-right:0}aside.sidebar.margin .admonition .admonition-title{margin-left:0;margin-right:0}@media (min-width:992px){aside.sidebar.margin{border:none}aside.sidebar.margin .admonition{margin:1rem 0;padding:0 0 1rem}}.cell.tag_margin,.cell.tag_popout,.margin.docutils.container,aside.margin,div.margin,figure.margin{z-index:2;position:relative;width:40%;float:right;background-color:unset;font-size:.9em;margin-left:.5rem}@media (min-width:992px),print{.cell.tag_margin,.cell.tag_popout,.margin.docutils.container,aside.margin,div.margin,figure.margin{width:33%;margin:0 -36% 0 0;clear:right}.cell.tag_margin p.sidebar-title,.cell.tag_popout p.sidebar-title,.margin.docutils.container p.sidebar-title,aside.margin p.sidebar-title,div.margin p.sidebar-title,figure.margin p.sidebar-title{margin-bottom:-1rem;border-bottom:none;padding-left:0}.cell.tag_margin p.sidebar-title~*,.cell.tag_popout p.sidebar-title~*,.margin.docutils.container p.sidebar-title~*,aside.margin p.sidebar-title~*,div.margin p.sidebar-title~*,figure.margin p.sidebar-title~*{padding-left:0;padding-right:0}}.cell.tag_margin div.cell.tag_margin .cell_output,.cell.tag_popout div.cell.tag_margin .cell_output,.margin.docutils.container div.cell.tag_margin .cell_output,aside.margin div.cell.tag_margin .cell_output,div.margin div.cell.tag_margin .cell_output,figure.margin div.cell.tag_margin .cell_output{padding-left:0}div.figure.margin-caption figcaption,div.figure.margin-caption p.caption,figure.margin-caption figcaption{z-index:2;position:relative;width:40%;float:right;background-color:unset;font-size:.9em;margin-left:.5rem}@media (min-width:992px),print{div.figure.margin-caption figcaption,div.figure.margin-caption p.caption,figure.margin-caption figcaption{width:33%;margin:0 -36% 0 0;clear:right}div.figure.margin-caption figcaption p.sidebar-title,div.figure.margin-caption p.caption p.sidebar-title,figure.margin-caption figcaption p.sidebar-title{margin-bottom:-1rem;border-bottom:none;padding-left:0}div.figure.margin-caption figcaption p.sidebar-title~*,div.figure.margin-caption p.caption p.sidebar-title~*,figure.margin-caption figcaption p.sidebar-title~*{padding-left:0;padding-right:0}}.margin-caption figcaption{text-align:left}div.cell.tag_full-width,div.cell.tag_full_width,div.full-width,div.full_width{z-index:2;position:relative}@media (min-width:992px){div.cell.tag_full-width,div.cell.tag_full_width,div.full-width,div.full_width{max-width:136%;width:136%}}blockquote.epigraph,blockquote.highlights,blockquote.pull-quote{font-size:1.25em;border-left:none;background-color:var(--pst-color-background)}blockquote div>p+p.attribution{font-style:normal;font-size:.9em;text-align:right;color:#6c757d;padding-right:2em}div[class*=highlight-],pre{clear:none}div.cell.tag_output_scroll div.cell_output,div.cell.tag_scroll-input div.cell_input,div.cell.tag_scroll-output div.cell_output{max-height:24em;overflow-y:auto}@media only print{div.utterances,hypothesis-sidebar{display:none}}.thebelab-cell{border:none!important;margin-right:.5em!important}.thebelab-cell .thebelab-input{padding-left:10px!important}.cell.docutils.container{padding-right:0!important}button.thebe-launch-button{height:2.5em;font-size:1em}
\ No newline at end of file
diff --git a/2023.2/_static/styles/theme.css b/2023.2/_static/styles/theme.css
new file mode 100644
index 0000000000..4519dd9120
--- /dev/null
+++ b/2023.2/_static/styles/theme.css
@@ -0,0 +1,2 @@
+/* Provided by Sphinx's 'basic' theme, and included in the final set of assets */
+@import "../basic.css";
diff --git a/2023.2/_static/vendor/fontawesome/6.1.2/LICENSE.txt b/2023.2/_static/vendor/fontawesome/6.1.2/LICENSE.txt
new file mode 100644
index 0000000000..cc557ece45
--- /dev/null
+++ b/2023.2/_static/vendor/fontawesome/6.1.2/LICENSE.txt
@@ -0,0 +1,165 @@
+Fonticons, Inc. (https://fontawesome.com)
+
+--------------------------------------------------------------------------------
+
+Font Awesome Free License
+
+Font Awesome Free is free, open source, and GPL friendly. You can use it for
+commercial projects, open source projects, or really almost whatever you want.
+Full Font Awesome Free license: https://fontawesome.com/license/free.
+
+--------------------------------------------------------------------------------
+
+# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/)
+
+The Font Awesome Free download is licensed under a Creative Commons
+Attribution 4.0 International License and applies to all icons packaged
+as SVG and JS file types.
+
+--------------------------------------------------------------------------------
+
+# Fonts: SIL OFL 1.1 License
+
+In the Font Awesome Free download, the SIL OFL license applies to all icons
+packaged as web and desktop font files.
+
+Copyright (c) 2022 Fonticons, Inc. (https://fontawesome.com)
+with Reserved Font Name: "Font Awesome".
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at:
+http://scripts.sil.org/OFL
+
+SIL OPEN FONT LICENSE
+Version 1.1 - 26 February 2007
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide
+development of collaborative font projects, to support the font creation
+efforts of academic and linguistic communities, and to provide a free and
+open framework in which fonts may be shared and improved in partnership
+with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and
+redistributed freely as long as they are not sold by themselves. The
+fonts, including any derivative works, can be bundled, embedded,
+redistributed and/or sold with any software provided that any reserved
+names are not used by derivative works. The fonts and derivatives,
+however, cannot be released under any other type of license. The
+requirement for fonts to remain under this license does not apply
+to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright
+Holder(s) under this license and clearly marked as such. This may
+include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the
+copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as
+distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting,
+or substituting — in part or in whole — any of the components of the
+Original Version, by changing formats or by porting the Font Software to a
+new environment.
+
+"Author" refers to any designer, engineer, programmer, technical
+writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of the Font Software, to use, study, copy, merge, embed, modify,
+redistribute, and sell modified and unmodified copies of the Font
+Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components,
+in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled,
+redistributed and/or sold with any software, provided that each copy
+contains the above copyright notice and this license. These can be
+included either as stand-alone text files, human-readable headers or
+in the appropriate machine-readable metadata fields within text or
+binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font
+Name(s) unless explicit written permission is granted by the corresponding
+Copyright Holder. This restriction only applies to the primary font name as
+presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
+Software shall not be used to promote, endorse or advertise any
+Modified Version, except to acknowledge the contribution(s) of the
+Copyright Holder(s) and the Author(s) or with their explicit written
+permission.
+
+5) The Font Software, modified or unmodified, in part or in whole,
+must be distributed entirely under this license, and must not be
+distributed under any other license. The requirement for fonts to
+remain under this license does not apply to any document created
+using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are
+not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
+OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
+OTHER DEALINGS IN THE FONT SOFTWARE.
+
+--------------------------------------------------------------------------------
+
+# Code: MIT License (https://opensource.org/licenses/MIT)
+
+In the Font Awesome Free download, the MIT license applies to all non-font and
+non-icon files.
+
+Copyright 2022 Fonticons, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in the
+Software without restriction, including without limitation the rights to use, copy,
+modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+and to permit persons to whom the Software is furnished to do so, subject to the
+following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+--------------------------------------------------------------------------------
+
+# Attribution
+
+Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font
+Awesome Free files already contain embedded comments with sufficient
+attribution, so you shouldn't need to do anything additional when using these
+files normally.
+
+We've kept attribution comments terse, so we ask that you do not actively work
+to remove them from files, especially code. They're a great way for folks to
+learn about Font Awesome.
+
+--------------------------------------------------------------------------------
+
+# Brand Icons
+
+All brand icons are trademarks of their respective owners. The use of these
+trademarks does not indicate endorsement of the trademark holder by Font
+Awesome, nor vice versa. **Please do not use brand logos for any purpose except
+to represent the company, product, or service to which they refer.**
diff --git a/2023.2/_static/vendor/fontawesome/6.1.2/css/all.min.css b/2023.2/_static/vendor/fontawesome/6.1.2/css/all.min.css
new file mode 100644
index 0000000000..aad4b1d9d3
--- /dev/null
+++ b/2023.2/_static/vendor/fontawesome/6.1.2/css/all.min.css
@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2022 Fonticons, Inc.
+ */.fa{font-family:var(--fa-style-family,"Font Awesome 6 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new file mode 100644
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+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2022 Fonticons, Inc.
+ */
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diff --git a/2023.2/_static/version_switcher.js b/2023.2/_static/version_switcher.js
new file mode 100644
index 0000000000..162851d217
--- /dev/null
+++ b/2023.2/_static/version_switcher.js
@@ -0,0 +1,31 @@
+function load_versions(json){
+    var button = document.getElementById('version-switcher-button')
+    var container = document.getElementById('version-switcher-dropdown')
+    var loc = window.location.href;
+    var s = document.createElement('select');
+    s.style = "border-radius:5px;"
+    const versions = JSON.parse(json);
+    for (entry of versions){
+        var o = document.createElement('option');
+        var optionText = '';
+        if ('name' in entry){
+            optionText = entry.name;
+        }else{
+            optionText = entry.version;
+        }
+        o.value = entry.url;
+        if (current_version == entry.version){
+            o.selected = true;
+        }
+        o.innerHTML = optionText;
+        s.append(o);
+    }
+    s.addEventListener("change", ()=> {
+        var current_url = new URL(window.location.href);
+        var path = current_url.pathname;
+        //strip version from path
+        var page_path = path.substring(project_name.length+current_version.length+3);
+        window.location.href = s.value + page_path;
+    });
+    container.append(s);
+}
diff --git a/2023.2/_static/webpack-macros.html b/2023.2/_static/webpack-macros.html
new file mode 100644
index 0000000000..14eb4bfda3
--- /dev/null
+++ b/2023.2/_static/webpack-macros.html
@@ -0,0 +1,31 @@
+<!--
+  AUTO-GENERATED from webpack.config.js, do **NOT** edit by hand.
+  These are re-used in layout.html
+-->
+{# Load FontAwesome icons #}
+{% macro head_pre_icons() %}
+  <link href="{{ pathto('_static/vendor/fontawesome/6.1.2/css/all.min.css', 1) }}?digest=5b4479735964841361fd" rel="stylesheet" />
+  <link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2', 1) }}" />
+{% endmacro %}
+
+{% macro head_pre_assets() %}
+  <!-- Loaded before other Sphinx assets -->
+  <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=5b4479735964841361fd" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/bootstrap.css', 1) }}?digest=5b4479735964841361fd" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=5b4479735964841361fd" rel="stylesheet" />
+{% endmacro %}
+
+{% macro head_js_preload() %}
+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=5b4479735964841361fd" />
+<link rel="preload" as="script" href="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=5b4479735964841361fd" />
+  <script src="{{ pathto('_static/vendor/fontawesome/6.1.2/js/all.min.js', 1) }}?digest=5b4479735964841361fd"></script>
+{% endmacro %}
+
+{% macro body_post() %}
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script src="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=5b4479735964841361fd"></script>
+<script src="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=5b4479735964841361fd"></script>
+{% endmacro %}
\ No newline at end of file
diff --git a/2023.2/acceleration.html b/2023.2/acceleration.html
index 36cf78f210..8fd26cff82 100644
--- a/2023.2/acceleration.html
+++ b/2023.2/acceleration.html
@@ -1,30 +1,34 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Acceleration &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="_static/custom.css" type="text/css" />
-      <link rel="stylesheet" href="_static/tabs.css" type="text/css" />
-    <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
+
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Performance Tuning Guide" href="guide/acceleration.html" />
-    <link rel="prev" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" href="kaggle/automl.html" />
+    <link rel="next" title="Patching" href="what-is-patching.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn*" href="index.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,36 +64,31 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Acceleration</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#speedup-over-original-scikit-learn">Speedup over original Scikit-learn</a><ul>
@@ -101,21 +97,34 @@
 </li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -155,10 +164,6 @@ <h2>Speedup over original Scikit-learn<a class="headerlink" href="#speedup-over-
 <li><p>HW: c5.24xlarge AWS EC2 Instance using an Intel Xeon Platinum 8275CL with 2 sockets and 24 cores per socket</p></li>
 <li><p>SW: scikit-learn version 0.24.2, scikit-learn-intelex version 2021.2.3, Python 3.8</p></li>
 </ul>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>You can also try out the benchmarks used to create this data by going to Intel’s <a class="reference external" href="https://github.com/IntelPython/scikit-learn_bench">scikit-learn_bench</a> repository and following the relevant instructions.</p>
-</div>
 <section id="kaggle-kernels">
 <h3>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Permalink to this heading"></a></h3>
 <p>Check out <a class="reference external" href="https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook">Introduction to scikit-learn-intelex</a>,
@@ -172,26 +177,14 @@ <h3>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Permalink
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="kaggle/automl.html" class="btn btn-neutral float-left" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="guide/acceleration.html" class="btn btn-neutral float-right" title="Performance Tuning Guide" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="index.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn*" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="what-is-patching.html" class="btn btn-neutral float-right" title="Patching" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b04bb190>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
-</a>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
-</a>
-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -204,6 +197,28 @@ <h3>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Permalink
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/algorithms.html b/2023.2/algorithms.html
index 8002f3435d..2f80fe01b0 100644
--- a/2023.2/algorithms.html
+++ b/2023.2/algorithms.html
@@ -1,30 +1,34 @@
+
+
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+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
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-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <link rel="index" title="Index" href="genindex.html" />
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-    <link rel="next" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" href="oneapi-gpu.html" />
-    <link rel="prev" title="Support" href="support.html" />
+    <link rel="next" title="Acceleration Details" href="guide/acceleration.html" />
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@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
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+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
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@@ -63,13 +64,46 @@
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 </div>
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-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      data-bs-toggle="dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Supported Algorithms</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#on-cpu">on CPU</a><ul>
@@ -92,43 +126,18 @@
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 </ul>
 </li>
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-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
 <li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
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-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
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-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
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-</ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
-</ul>
+
 
         </div>
       </div>
@@ -662,26 +671,14 @@ <h2>Scikit-learn tests<a class="headerlink" href="#scikit-learn-tests" title="Pe
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@@ -694,6 +691,28 @@ <h2>Scikit-learn tests<a class="headerlink" href="#scikit-learn-tests" title="Pe
       </div>
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+  <div class="version-switcher__container dropdown">
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/2023.2/blogs.html b/2023.2/blogs.html
index 770ca0bd2b..8066001888 100644
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+++ b/2023.2/blogs.html
@@ -1,30 +1,34 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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+    <link rel="next" title="System requirements and supported configurations" href="system-requirements.html" />
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
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@@ -63,53 +64,61 @@
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+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
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-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
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 </ul>
 
+
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@@ -150,7 +159,6 @@
 <li><p><a class="reference external" href="https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e">Accelerate Your scikit-learn Applications</a>,</p></li>
 <li><p><a class="reference external" href="https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe">Accelerate Linear Models for Machine Learning</a>,</p></li>
 <li><p><a class="reference external" href="https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1">Accelerate K-Means Clustering</a>.</p></li>
-<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/why-pay-more-for-machine-learning-893683bd78e4">Why Pay More for Machine Learning?</a>.</p></li>
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@@ -158,26 +166,14 @@
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@@ -190,6 +186,28 @@
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+    >
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-    <link rel="next" title="Verbose Mode" href="verbose.html" />
-    <link rel="prev" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" href="oneapi-gpu.html" />
+    <link rel="next" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" href="oneapi-gpu.html" />
+    <link rel="prev" title="Verbose Mode" href="verbose.html" />
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           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,53 +64,61 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
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 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
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-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
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     </nav>
@@ -151,26 +160,14 @@
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           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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-        <a href="verbose.html" class="btn btn-neutral float-right" title="Verbose Mode" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="verbose.html" class="btn btn-neutral float-left" title="Verbose Mode" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="oneapi-gpu.html" class="btn btn-neutral float-right" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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   <hr/>
 
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-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
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-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
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+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
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@@ -183,6 +180,28 @@
       </div>
     </section>
   </div>
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+    <script>
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
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diff --git a/2023.2/genindex.html b/2023.2/genindex.html
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+
+
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+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Index &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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+      <link rel="stylesheet" type="text/css" href="_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
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-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
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+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -48,11 +52,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -61,52 +62,60 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
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 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
-</ul>
+
 
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       </div>
@@ -173,19 +182,7 @@ <h2 id="U">U</h2>
   <hr/>
 
   <div role="contentinfo">
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-    Do Not Share My Personal Information
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-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
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@@ -198,6 +195,28 @@ <h2 id="U">U</h2>
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     </section>
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+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
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+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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+    <link rel="next" title="Verbose Mode" href="verbose.html" />
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@@ -48,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
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   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -61,52 +64,68 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
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 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
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-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Global Patching</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#patch-all-supported-algorithms">Patch all supported algorithms</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#patch-selected-algorithms">Patch selected algorithms</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#disable-patching-notifications">Disable patching notifications</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#disable-global-patching">Disable global patching</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#enable-global-patching-via-code">Enable global patching via code</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#disable-global-patching-via-code">Disable global patching via code</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
 </ul>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
-</ul>
+
 
         </div>
       </div>
@@ -206,24 +225,15 @@ <h2>Disable global patching via code<a class="headerlink" href="#disable-global-
 
            </div>
           </div>
-          <footer>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="guide/acceleration.html" class="btn btn-neutral float-left" title="Acceleration Details" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="verbose.html" class="btn btn-neutral float-right" title="Verbose Mode" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
 
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0923fa0>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
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-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
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+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -236,6 +246,28 @@ <h2>Disable global patching via code<a class="headerlink" href="#disable-global-
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
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diff --git a/2023.2/guide/acceleration.html b/2023.2/guide/acceleration.html
index 75fc5dff2c..b6c185628f 100644
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+
+
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+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-  <!--[if lt IE 9]>
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-  <![endif]-->
+  <title>Acceleration Details &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
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+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
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-    <link rel="next" title="Follow us on Medium" href="../blogs.html" />
-    <link rel="prev" title="Acceleration" href="../acceleration.html" />
+    <link rel="next" title="Global Patching" href="../global-patching.html" />
+    <link rel="prev" title="Supported Algorithms" href="../algorithms.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -50,11 +54,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,57 +64,65 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Performance Tuning Guide</a><ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Acceleration Details</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#tsne">TSNE</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#random-forest">Random Forest</a></li>
 </ul>
 </li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
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       </div>
     </nav>
@@ -128,7 +137,7 @@
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
       <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
-      <li class="breadcrumb-item active">Performance Tuning Guide</li>
+      <li class="breadcrumb-item active">Acceleration Details</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/guide/acceleration.rst.txt" rel="nofollow"> View page source</a>
       </li>
@@ -138,8 +147,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <section id="performance-tuning-guide">
-<h1>Performance Tuning Guide<a class="headerlink" href="#performance-tuning-guide" title="Permalink to this heading"></a></h1>
+  <section id="acceleration-details">
+<h1>Acceleration Details<a class="headerlink" href="#acceleration-details" title="Permalink to this heading"></a></h1>
 <p>The performance of some algorithms changes based on the parameters that are used.
 This section denotes the details of such cases.</p>
 <p>Refer to <a class="reference internal" href="../algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a> to see the full list of algorithms, parameters, and data formats supported in Intel® Extension for Scikit-learn*.</p>
@@ -218,26 +227,14 @@ <h1>Performance Tuning Guide<a class="headerlink" href="#performance-tuning-guid
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="../acceleration.html" class="btn btn-neutral float-left" title="Acceleration" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="../blogs.html" class="btn btn-neutral float-right" title="Follow us on Medium" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="../algorithms.html" class="btn btn-neutral float-left" title="Supported Algorithms" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../global-patching.html" class="btn btn-neutral float-right" title="Global Patching" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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@@ -250,6 +247,28 @@ <h1>Performance Tuning Guide<a class="headerlink" href="#performance-tuning-guid
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
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+      data-bs-toggle="dropdown"
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+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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diff --git a/2023.2/index.html b/2023.2/index.html
index 4b845085cb..d8a74ed2b9 100644
--- a/2023.2/index.html
+++ b/2023.2/index.html
@@ -1,29 +1,33 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Intel® Extension for Scikit-learn* &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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+      <link rel="stylesheet" type="text/css" href="_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
+
   
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-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
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+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
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-    <link rel="next" title="Quick Start" href="quick-start.html" />
+    <link rel="next" title="Acceleration" href="acceleration.html" />
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     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -49,11 +53,8 @@
           
           <a href="#" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -62,52 +63,60 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
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 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
-</ul>
+
 
         </div>
       </div>
@@ -175,7 +184,7 @@ <h2>Usage<a class="headerlink" href="#usage" title="Permalink to this heading">
 </pre></div>
 </div>
 </li>
-<li><p>Through <a class="reference internal" href="what-is-patching.html#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
+<li><p>Through <a class="reference internal" href="global-patching.html#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
 </pre></div>
 </div>
@@ -237,16 +246,6 @@ <h2>Important Links<a class="headerlink" href="#important-links" title="Permalin
 </div>
 <div class="toctree-wrapper compound">
 </div>
-<div class="toctree-wrapper compound">
-</div>
-<div class="toctree-wrapper compound">
-</div>
-<div class="toctree-wrapper compound">
-</div>
-<div class="toctree-wrapper compound">
-</div>
-<div class="toctree-wrapper compound">
-</div>
 </section>
 </section>
 
@@ -254,25 +253,13 @@ <h2>Important Links<a class="headerlink" href="#important-links" title="Permalin
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="quick-start.html" class="btn btn-neutral float-right" title="Quick Start" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="acceleration.html" class="btn btn-neutral float-right" title="Acceleration" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
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-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+    <p>&#169; Copyright 2022, Intel.</p>
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@@ -285,6 +272,28 @@ <h2>Important Links<a class="headerlink" href="#important-links" title="Permalin
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
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   <script>
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           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/installation.html b/2023.2/installation.html
index 7afaae97a8..2bb18d849f 100644
--- a/2023.2/installation.html
+++ b/2023.2/installation.html
@@ -1,31 +1,35 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
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+    <link rel="next" title="Quick Start" href="quick-start.html" />
+    <link rel="prev" title="Memory Requirements" href="memory-requirements.html" />
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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@@ -51,11 +55,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -64,25 +65,39 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+    >
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Installation</a><ul>
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-</ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
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 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
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+
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     </nav>
@@ -315,6 +324,7 @@ <h3>Build Intel® Extension for Scikit-learn*<a class="headerlink" href="#build-
 <h3>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this heading"></a></h3>
 <ul class="simple">
 <li><p><a class="reference internal" href="what-is-patching.html#what-is-patching"><span class="std std-ref">What is patching and how to patch scikit-learn</span></a></p></li>
+<li><p><a class="reference internal" href="quick-start.html#get-started"><span class="std std-ref">How to use scikit-learn-intelex</span></a></p></li>
 </ul>
 </section>
 </section>
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+        <a href="memory-requirements.html" class="btn btn-neutral float-left" title="Memory Requirements" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="quick-start.html" class="btn btn-neutral float-right" title="Quick Start" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+    <p>&#169; Copyright 2022, Intel.</p>
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@@ -356,6 +354,28 @@ <h3>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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diff --git a/2023.2/kaggle.html b/2023.2/kaggle.html
index f866e41375..8c95bd4c32 100644
--- a/2023.2/kaggle.html
+++ b/2023.2/kaggle.html
@@ -1,23 +1,26 @@
+
+
 <!DOCTYPE html>
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+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
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+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
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+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -25,6 +28,7 @@
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
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@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,32 +64,42 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Kaggle Kernels</a><ul>
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@@ -97,24 +108,22 @@
 </ul>
 </li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
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       </div>
     </nav>
@@ -183,19 +192,7 @@
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-<jinja2.runtime.BlockReference object at 0x7f57b08721f0>
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-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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-
+    <p>&#169; Copyright 2022, Intel.</p>
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@@ -208,6 +205,28 @@
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     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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diff --git a/2023.2/kaggle/automl.html b/2023.2/kaggle/automl.html
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@@ -1,30 +1,34 @@
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+<html class="writer-html5" lang="en">
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+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js"></script>
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-    <link rel="next" title="Acceleration" href="../acceleration.html" />
+    <link rel="next" title="Supported Algorithms" href="../algorithms.html" />
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@@ -50,11 +54,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
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-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+    <script>
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+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
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+    >
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
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-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
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@@ -189,25 +198,13 @@ <h1>Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*<a cla
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@@ -424,19 +433,7 @@ <h2>Classification Tasks in Natural Language Processing<a class="headerlink" hre
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
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 </ul>
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+    >
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+      <span class="caret"></span>
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-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
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-           <div itemprop="articleBody">
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+++ b/2023.2/memory-requirements.html
@@ -1,30 +1,34 @@
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+<html class="writer-html5" lang="en">
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-  <!--[if lt IE 9]>
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+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="_static/custom.css" />
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+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Samples" href="samples.html" />
+    <link rel="next" title="Installation" href="installation.html" />
     <link rel="prev" title="System requirements and supported configurations" href="system-requirements.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
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@@ -50,11 +54,8 @@
           
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             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
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-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
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 </ul>
 </li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
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     </nav>
@@ -173,25 +182,13 @@ <h2>Memory consumption on GPU<a class="headerlink" href="#memory-consumption-on-
           </div>
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+        <a href="installation.html" class="btn btn-neutral float-right" title="Installation" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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@@ -204,6 +201,28 @@ <h2>Memory consumption on GPU<a class="headerlink" href="#memory-consumption-on-
       </div>
     </section>
   </div>
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+    <script>
+      current_version = "2023.2"
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diff --git a/2023.2/objects.inv b/2023.2/objects.inv
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index 21e759566d..ac8e57ed29 100644
--- a/2023.2/oneapi-gpu.html
+++ b/2023.2/oneapi-gpu.html
@@ -1,30 +1,34 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
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@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,57 +64,65 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
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+    >
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+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">oneAPI and GPU support</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
-</ul>
+
 
         </div>
       </div>
@@ -228,12 +237,9 @@ <h2>Example<a class="headerlink" href="#example" title="Permalink to this headin
    <span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Current offloading behavior restricts fitting and inference of any models to be
+<p>Note: Current offloading behavior restricts fitting and inference of any models to be
 in the same context or absence of context. For example, a model trained in the GPU context with
 target_offload=”gpu:0” throws an error if the inference is made outside the same GPU context.</p>
-</div>
 </section>
 </section>
 
@@ -241,26 +247,14 @@ <h2>Example<a class="headerlink" href="#example" title="Permalink to this headin
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+    <p>&#169; Copyright 2022, Intel.</p>
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@@ -273,6 +267,28 @@ <h2>Example<a class="headerlink" href="#example" title="Permalink to this headin
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+  <div class="version-switcher__container dropdown">
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diff --git a/2023.2/quick-start.html b/2023.2/quick-start.html
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+++ b/2023.2/quick-start.html
@@ -1,30 +1,34 @@
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+<html class="writer-html5" lang="en">
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@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,53 +64,61 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Quick Start</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
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+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -135,7 +144,7 @@
            <div itemprop="articleBody">
              
   <section id="quick-start">
-<span id="id1"></span><h1>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this heading"></a></h1>
+<span id="get-started"></span><h1>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this heading"></a></h1>
 <p>You may enable patching in different ways:</p>
 <div class="admonition important">
 <p class="admonition-title">Important</p>
@@ -165,7 +174,7 @@
 </pre></div>
 </div>
 </li>
-<li><p>Through <a class="reference internal" href="what-is-patching.html#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
+<li><p>Through <a class="reference internal" href="global-patching.html#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
 </pre></div>
 </div>
@@ -216,7 +225,6 @@
 <li><p><a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported algorithms and parameters</span></a></p></li>
 <li><p><a class="reference internal" href="samples.html#samples"><span class="std std-ref">Samples</span></a></p></li>
 <li><p><a class="reference internal" href="global-patching.html#global-patching"><span class="std std-ref">Global Patching</span></a></p></li>
-<li><p><a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/guide/intel-extension-for-scikit-learn-getting-started.html">Getting Started Guide</a></p></li>
 </ul>
 </div>
 </section>
@@ -225,26 +233,14 @@
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           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+    <p>&#169; Copyright 2022, Intel.</p>
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@@ -257,6 +253,28 @@
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   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
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+    <div id="version-switcher-button"
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+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
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       jQuery(function () {
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diff --git a/2023.2/samples.html b/2023.2/samples.html
index 558fded632..3e6d6ae42c 100644
--- a/2023.2/samples.html
+++ b/2023.2/samples.html
@@ -1,30 +1,34 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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-    <link rel="prev" title="Memory Requirements" href="memory-requirements.html" />
+    <link rel="prev" title="Quick Start" href="quick-start.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
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@@ -63,32 +64,42 @@
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-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Samples</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -101,29 +112,25 @@
 <li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
-</ul>
+
 
         </div>
       </div>
@@ -179,47 +186,24 @@
 <li class="toctree-l1"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 </ul>
 </div>
-<p class="rubric">Jupyter Notebook Samples</p>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l1"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
-</div>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p>There are also <a class="reference internal" href="kaggle.html#kaggle"><span class="std std-ref">Kaggle kernels</span></a> that use Intel® Extension for Scikit-learn* for a variety of machine learning scenarios.</p>
 </div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Explore the complete list of oneAPI code samples in the <a class="reference external" href="https://oneapi-src.github.io/oneAPI-samples/">oneAPI Samples Catalog</a>. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.</p>
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+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
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+    >
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diff --git a/2023.2/samples/ElasticNet.html b/2023.2/samples/ElasticNet.html
index 09b738129a..194fe1a5de 100644
--- a/2023.2/samples/ElasticNet.html
+++ b/2023.2/samples/ElasticNet.html
@@ -1,26 +1,29 @@
+
+
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+<html class="writer-html5" lang="en">
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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@@ -28,6 +31,7 @@
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
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-                <a href="../../versions.html">2023.2 ▼</a>
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   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+    >
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -110,30 +121,26 @@
 <li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
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-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -266,7 +273,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
@@ -415,19 +422,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b007ec10>
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-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
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-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -440,6 +435,28 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
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+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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diff --git a/2023.2/samples/ElasticNet.ipynb b/2023.2/samples/ElasticNet.ipynb
index 9ffdd8fe1c..29e5dca851 100644
--- a/2023.2/samples/ElasticNet.ipynb
+++ b/2023.2/samples/ElasticNet.ipynb
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    "metadata": {},
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-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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--- a/2023.2/samples/daal4py_data_science.html
+++ b/2023.2/samples/daal4py_data_science.html
@@ -1,31 +1,35 @@
+
+
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+<html class="writer-html5" lang="en">
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -51,11 +55,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
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 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -64,52 +65,60 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
-</ul>
+
 
         </div>
       </div>
@@ -583,19 +592,7 @@ <h2>Migrating the trained model for inference on external systems<a class="heade
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b092f1c0>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
-</a>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
-</a>
-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -608,6 +605,28 @@ <h2>Migrating the trained model for inference on external systems<a class="heade
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/samples/dbscan.html b/2023.2/samples/dbscan.html
index 670282eb75..48efad6bad 100644
--- a/2023.2/samples/dbscan.html
+++ b/2023.2/samples/dbscan.html
@@ -1,26 +1,29 @@
+
+
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+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
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-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +31,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset" href="kmeans.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
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@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -110,30 +121,26 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -241,7 +248,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -384,19 +391,7 @@ <h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b04b29d0>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
-</a>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
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-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -409,6 +404,28 @@ <h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
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           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/samples/dbscan.ipynb b/2023.2/samples/dbscan.ipynb
index 829719629a..d6e5c92653 100644
--- a/2023.2/samples/dbscan.ipynb
+++ b/2023.2/samples/dbscan.ipynb
@@ -128,7 +128,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/2023.2/samples/kmeans.html b/2023.2/samples/kmeans.html
index da7738fcc5..6afbd471d1 100644
--- a/2023.2/samples/kmeans.html
+++ b/2023.2/samples/kmeans.html
@@ -1,26 +1,29 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +31,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" href="ridge_regression.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
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   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -110,30 +121,26 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
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-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -250,7 +257,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -404,19 +411,7 @@ <h3>Compare inertia and number of iterations of patched Scikit-learn and origina
   <hr/>
 
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-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
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-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -429,6 +424,28 @@ <h3>Compare inertia and number of iterations of patched Scikit-learn and origina
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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diff --git a/2023.2/samples/kmeans.ipynb b/2023.2/samples/kmeans.ipynb
index 8e6470f568..df09f8ded5 100644
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+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/2023.2/samples/knn_mnist.html b/2023.2/samples/knn_mnist.html
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--- a/2023.2/samples/knn_mnist.html
+++ b/2023.2/samples/knn_mnist.html
@@ -1,26 +1,29 @@
+
+
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+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Intel® Extension for Scikit-learn KNN for MNIST dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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-  <![endif]-->
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
   
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-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <link rel="index" title="Index" href="../genindex.html" />
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@@ -28,6 +31,7 @@
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
@@ -109,30 +120,26 @@
 <li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -223,7 +230,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -396,19 +403,7 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57afd15430>
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-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
-</a>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
-</a>
-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -421,6 +416,28 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+  </div>
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diff --git a/2023.2/samples/knn_mnist.ipynb b/2023.2/samples/knn_mnist.ipynb
index 6b7e2465a0..b8604d70f0 100644
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    "id": "6bb14ac8",
    "metadata": {},
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-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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@@ -1,26 +1,29 @@
+
+
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+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
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-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +31,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" href="ElasticNet.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -110,30 +121,26 @@
 <li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -273,7 +280,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -421,19 +428,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0596100>
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+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
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@@ -446,6 +441,28 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
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diff --git a/2023.2/samples/lasso_regression.ipynb b/2023.2/samples/lasso_regression.ipynb
index 77571c2432..967d0d4e54 100644
--- a/2023.2/samples/lasso_regression.ipynb
+++ b/2023.2/samples/lasso_regression.ipynb
@@ -162,7 +162,7 @@
    "id": "20c5ab48",
    "metadata": {},
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-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/2023.2/samples/linear_regression.html b/2023.2/samples/linear_regression.html
index ebde22f2d2..4c5bb11fd3 100644
--- a/2023.2/samples/linear_regression.html
+++ b/2023.2/samples/linear_regression.html
@@ -1,26 +1,29 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
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+
   
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +31,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset" href="lasso_regression.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
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@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -110,30 +121,26 @@
 <li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -273,7 +280,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -416,19 +423,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0a90610>
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-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
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-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
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-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -441,6 +436,28 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
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+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+  </div>
   <script>
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diff --git a/2023.2/samples/linear_regression.ipynb b/2023.2/samples/linear_regression.ipynb
index 4a356e6bfc..508ee06d8c 100644
--- a/2023.2/samples/linear_regression.ipynb
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@@ -162,7 +162,7 @@
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+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/2023.2/samples/logistictic_regression_cifar.html b/2023.2/samples/logistictic_regression_cifar.html
index 9ef5cae06f..6681abace1 100644
--- a/2023.2/samples/logistictic_regression_cifar.html
+++ b/2023.2/samples/logistictic_regression_cifar.html
@@ -1,26 +1,29 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
   
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+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +31,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="knn_mnist.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Logistic Regression for Cifar dataset</a><ul>
@@ -109,30 +120,26 @@
 <li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -227,7 +234,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -375,19 +382,7 @@ <h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss me
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0b172b0>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
-</a>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
-</a>
-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -400,6 +395,28 @@ <h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss me
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/samples/logistictic_regression_cifar.ipynb b/2023.2/samples/logistictic_regression_cifar.ipynb
index f4cb2b95a7..43727804d7 100644
--- a/2023.2/samples/logistictic_regression_cifar.ipynb
+++ b/2023.2/samples/logistictic_regression_cifar.ipynb
@@ -108,7 +108,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/2023.2/samples/nusvr_medical_charges.html b/2023.2/samples/nusvr_medical_charges.html
index fcebb94a1c..b04cdadcad 100644
--- a/2023.2/samples/nusvr_medical_charges.html
+++ b/2023.2/samples/nusvr_medical_charges.html
@@ -1,26 +1,29 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-  <![endif]-->
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +31,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" href="linear_regression.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -110,30 +121,26 @@
 <li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -239,7 +246,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
@@ -385,19 +392,7 @@ <h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patche
   <hr/>
 
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-<jinja2.runtime.BlockReference object at 0x7f57b0c53220>
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-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
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-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
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-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
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@@ -410,6 +405,28 @@ <h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patche
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
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       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/samples/nusvr_medical_charges.ipynb b/2023.2/samples/nusvr_medical_charges.ipynb
index bcfbe9154f..8c72c1b71d 100644
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+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/2023.2/samples/random_forest_yolanda.html b/2023.2/samples/random_forest_yolanda.html
index b50f5ee8ab..3bbaf7c920 100644
--- a/2023.2/samples/random_forest_yolanda.html
+++ b/2023.2/samples/random_forest_yolanda.html
@@ -1,26 +1,29 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
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+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +31,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset" href="nusvr_medical_charges.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -109,30 +120,26 @@
 <li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -223,7 +230,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -366,19 +373,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0c8bdc0>
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@@ -391,6 +386,28 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
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diff --git a/2023.2/samples/random_forest_yolanda.ipynb b/2023.2/samples/random_forest_yolanda.ipynb
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+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +31,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" href="random_forest_yolanda.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -110,30 +121,26 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -271,7 +278,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
@@ -419,19 +426,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0710400>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
-</a>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
-</a>
-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -444,6 +439,28 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/samples/ridge_regression.ipynb b/2023.2/samples/ridge_regression.ipynb
index c3d0856d00..1c159a13ae 100644
--- a/2023.2/samples/ridge_regression.ipynb
+++ b/2023.2/samples/ridge_regression.ipynb
@@ -169,7 +169,7 @@
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    "metadata": {},
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-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/2023.2/samples/svc_adult.html b/2023.2/samples/svc_adult.html
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--- a/2023.2/samples/svc_adult.html
+++ b/2023.2/samples/svc_adult.html
@@ -1,26 +1,29 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Intel® Extension for Scikit-learn SVC for Adult dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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-  <![endif]-->
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +31,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset" href="logistictic_regression_cifar.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -53,11 +57,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +67,42 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
@@ -109,30 +120,26 @@
 <li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -371,19 +378,7 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0b04d90>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
-</a>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
-</a>
-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -396,6 +391,28 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/samples/tsne.html b/2023.2/samples/tsne.html
index 5dac9c4ec3..708bbd823e 100644
--- a/2023.2/samples/tsne.html
+++ b/2023.2/samples/tsne.html
@@ -1,31 +1,35 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Intel® Extension for Scikit-learn TSNE example &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-  <![endif]-->
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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@@ -51,11 +55,8 @@
           
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="../_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -64,52 +65,60 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
-</ul>
+
 
         </div>
       </div>
@@ -325,19 +334,7 @@ <h2>Plot embeddings original scikit-learn and Intel® extension<a class="headerl
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0c53eb0>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
-    Cookies
-</a>
-<a href=
-    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
-    Privacy
-</a>
-<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
-    Do Not Share My Personal Information
-</a>
-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
@@ -350,6 +347,28 @@ <h2>Plot embeddings original scikit-learn and Intel® extension<a class="headerl
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2023.2/search.html b/2023.2/search.html
index 0ad2e2d27a..bc53b949a4 100644
--- a/2023.2/search.html
+++ b/2023.2/search.html
@@ -1,24 +1,27 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Search &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="_static/custom.css" type="text/css" />
-      <link rel="stylesheet" href="_static/tabs.css" type="text/css" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
+
+  
     <link rel="shortcut icon" href="_static/favicons.png"/>
     
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
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-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <script src="_static/searchtools.js"></script>
     <script src="_static/language_data.js"></script>
@@ -26,6 +29,7 @@
     <link rel="search" title="Search" href="#" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -51,11 +55,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="#" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -64,52 +65,60 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
 <li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
 <li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
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index 625233d080..dda891a44e 100644
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   <title>Support &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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-    <link rel="next" title="Supported Algorithms" href="algorithms.html" />
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+    <link rel="next" title="How to Contribute" href="contribute.html" />
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           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
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@@ -63,53 +64,61 @@
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-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
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 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
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 <ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
-<ul>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
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       </div>
     </nav>
@@ -149,26 +158,14 @@ <h1>Support<a class="headerlink" href="#support" title="Permalink to this headin
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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-        <a href="algorithms.html" class="btn btn-neutral float-right" title="Supported Algorithms" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="oneapi-gpu.html" class="btn btn-neutral float-left" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="contribute.html" class="btn btn-neutral float-right" title="How to Contribute" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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-    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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-
+    <p>&#169; Copyright 2022, Intel.</p>
   </div>
 
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@@ -181,6 +178,28 @@ <h1>Support<a class="headerlink" href="#support" title="Permalink to this headin
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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diff --git a/2023.2/system-requirements.html b/2023.2/system-requirements.html
index 38ff30cc56..5aea730c73 100644
--- a/2023.2/system-requirements.html
+++ b/2023.2/system-requirements.html
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+
+
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+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>System requirements and supported configurations &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <link rel="next" title="Memory Requirements" href="memory-requirements.html" />
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     <script type="text/javascript">
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@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,27 +64,35 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">System Requirements</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#supported-configurations">Supported configurations</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#pypi-channel">PyPI channel</a></li>
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
 <li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
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       </div>
     </nav>
@@ -387,26 +396,14 @@ <h3>For GPU<a class="headerlink" href="#for-gpu" title="Permalink to this headin
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+        <a href="blogs.html" class="btn btn-neutral float-left" title="Follow us on Medium" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
         <a href="memory-requirements.html" class="btn btn-neutral float-right" title="Memory Requirements" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+    <p>&#169; Copyright 2022, Intel.</p>
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@@ -419,6 +416,28 @@ <h3>For GPU<a class="headerlink" href="#for-gpu" title="Permalink to this headin
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
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+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
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+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+    </div>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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diff --git a/2023.2/verbose.html b/2023.2/verbose.html
index 091c3521e8..b807296dbb 100644
--- a/2023.2/verbose.html
+++ b/2023.2/verbose.html
@@ -1,30 +1,34 @@
+
+
 <!DOCTYPE html>
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+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
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@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
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-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
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@@ -63,53 +64,61 @@
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-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
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+      <!-- dropdown will be populated by javascript on page load -->
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+  
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+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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diff --git a/2023.2/what-is-patching.html b/2023.2/what-is-patching.html
index 6ca4484c51..f260798e55 100644
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+++ b/2023.2/what-is-patching.html
@@ -1,30 +1,34 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -50,11 +54,8 @@
           
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
+              <img src="_static/oneAPI-rgb-rev-100.png" class="logo" alt="Logo"/>
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
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@@ -63,52 +64,63 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2023.2"
+      project_name = "scikit-learn-intelex"
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+      <!-- dropdown will be populated by javascript on page load -->
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">About</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Patching</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Patching</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#how-it-works">How it works</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
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 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Acceleration Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="global-patching.html">Global Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">Community</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
-</ul>
+
 
         </div>
       </div>
@@ -168,61 +180,25 @@
 </pre></div>
 </div>
 </li>
-<li><p>Through <a class="reference internal" href="#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
+<li><p>Through <a class="reference internal" href="global-patching.html#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
 </pre></div>
 </div>
 </li>
 </ul>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="quick-start.html#get-started"><span class="std std-ref">Quick Start</span></a></p>
+</div>
 </dd>
-<dt id="term-global-patching">global patching<a class="headerlink" href="#term-global-patching" title="Permalink to this term"></a></dt><dd><p id="global-patching">Use global patching to patch all your scikit-learn applications without any additional actions.</p>
-<p class="rubric">Prerequisites</p>
-<ul class="simple">
-<li><p>Intel® Extension for Scikit-learn*</p></li>
-<li><p>Scikit-learn</p></li>
-<li><p>read and write permissions to Scikit-learn files</p></li>
-</ul>
-<p>To patch all <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">supported algorithms</span></a>, run:</p>
+<dt id="term-global-patching">global patching<a class="headerlink" href="#term-global-patching" title="Permalink to this term"></a></dt><dd><p>One of the patching options available in Intel® Extension for Scikit-learn*.
+With global patching, you can patch all scikit-learn applications at once:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
 </pre></div>
 </div>
-<p>If you want to patch only some algorithms, use <code class="docutils literal notranslate"><span class="pre">--algorithm</span></code> or <code class="docutils literal notranslate"><span class="pre">-a</span></code> keys
-with a list of algorithms to patch.</p>
-<p>For example, to patch only SVC and RandomForestClassifier estimators, run:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span>
-</pre></div>
-</div>
-<p>If you do not want to receive patching notifications, then use <code class="docutils literal notranslate"><span class="pre">--no-verbose</span></code> or <code class="docutils literal notranslate"><span class="pre">-nv</span></code> keys:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span> <span class="o">-</span><span class="n">nv</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you run the global patching command several times with different parameters,
-then only the last configuration will be applied.</p>
-</div>
-<p>To disable global patching, use the following command:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">unpatch_sklearn</span>
-</pre></div>
-</div>
-<p>You can also enable global patching in your code. To do this,
-use the <code class="docutils literal notranslate"><span class="pre">patch_sklearn</span></code> function with the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
-<span class="n">patch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="kn">import</span> <span class="nn">sklearn</span>
-</pre></div>
-</div>
-<p>After that, Scikit-learn patches will be enabled in the current application and
-in all others that use the same environment.</p>
-<p>To disable global patching via code, use the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code>
-argument in the <code class="docutils literal notranslate"><span class="pre">unpatch_sklearn</span></code> function:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
-<span class="n">unpatch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you clone an environment with enabled global patching, it will already be applied in the new environment.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="global-patching.html#global-patching"><span class="std std-ref">Global Patching</span></a></p>
 </div>
 </dd>
 <dt id="term-unpatching">unpatching<a class="headerlink" href="#term-unpatching" title="Permalink to this term"></a></dt><dd><p>To undo the patch is to return to the use of original scikit-learn implementation and
@@ -251,26 +227,14 @@ <h2>How it works<a class="headerlink" href="#how-it-works" title="Permalink to t
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@@ -283,6 +247,28 @@ <h2>How it works<a class="headerlink" href="#how-it-works" title="Permalink to t
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+
+
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   <title>Page not found &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
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             Intel(R) Extension for Scikit-learn*
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@@ -93,205 +84,51 @@
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+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-from-pypi">Install from PyPI</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#acceleration">Acceleration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/support.html">Support</a></li>
 </ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#classification">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#regression">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#other-tasks">Other tasks</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id2">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id5">Nearest Neighbors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#device-offloading">Device offloading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#example">Example</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html">oneAPI and GPU support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/distributed-mode.html">Distributed Mode</a></li>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/verbose.html">Verbose Mode</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/deprecation.html">Deprecation Notice</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/guide/acceleration.html#random-forest">Random Forest</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/tutorials.html#case-studies">Case Studies</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/support.html#issues">Issues</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/contribute.html">How to Contribute</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/contribute.html#pull-requests">Pull Requests</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/license.html">License</a></li>
 </ul>
 
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+++ b/2024.3/_sources/contribute.rst.txt
@@ -14,51 +14,40 @@
 .. * limitations under the License.
 .. *******************************************************************************/
 
-##################
 How to Contribute
-##################
+-----------------
 
-As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn. 
-This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.
+We welcome community contributions to |intelex|. You can:
 
-Licensing
-----------------------------------
-Intel(R) Extension for Scikit-learn uses the `Apache 2.0 License <https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE>`_. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.
+- Submit your changes directly with a `pull request <https://github.com/intel/scikit-learn-intelex/pulls>`_.
+- Log a bug or make a feature request with an `issue <https://github.com/intel/scikit-learn-intelex/issues>`_.
 
-Pull Requests
-----------------------------------
-No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.
-
-Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:
-
-::
-
-  git config --global user.name "Kate Developer"
-  git config --global user.email kate.developer@company.com
+Refer to our guidelines on pull requests and issues before you proceed.
 
-Before Contributing Changes
-********************************************
+Issues
+******
 
-Make sure you can build the product and run all the tests with your patch.
-For a larger feature, provide a relevant test.
-Document your code. Our project uses reStructuredText for documentation.
-For new file(s), specify the appropriate copyright year in the first line.
-Submit a pull request into the master branch.
-Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.
+Use `GitHub issues <https://github.com/intel/scikit-learn-intelex/issues>`_ to:
 
-Code Style
--------------------
+- report an issue
+- make a feature request
 
-We use `black <https://black.readthedocs.io/en/stable/>`_ and `isort <https://pycqa.github.io/isort/>`_ formatters for Python* code. 
-The line length is 90 characters; use default options otherwise. You can find the linter configuration in `.pyproject.toml <https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml>`_.
+.. note::
+   To report a vulnerability, refer to
+   `Intel vulnerability reporting policy <https://www.intel.com/content/www/us/en/security-center/default.html>`_.
 
-A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.
-
-Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:
-
-::
+Pull Requests
+*************
 
-  pip install pre-commit
-  pre-commit install
+To contribute your changes directly to the repository, do the following:
 
+- Make sure you can build the product and run all the examples with your patch.
+- For a larger feature, provide a relevant example.
+- Document your code.
+- `Submit <https://github.com/intel/scikit-learn-intelex/pulls>`_ a pull request into the `main` branch.
+  Provide a brief description of the changes you are contributing.
 
+Public CI is enabled for the repository. Your PR should pass all of our checks.
+We will review your contribution and, if any additional fixes or modifications are necessary,
+we may give some feedback to guide you.
+When accepted, your pull request will be merged into our GitHub repository.
diff --git a/2024.3/_sources/global-patching.rst.txt b/2024.3/_sources/global-patching.rst.txt
new file mode 100644
index 0000000000..88a4c050d6
--- /dev/null
+++ b/2024.3/_sources/global-patching.rst.txt
@@ -0,0 +1,89 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _global_patching:
+
+###############
+Global Patching
+###############
+
+Use global patching to patch all your scikit-learn applications without any additional actions.
+
+.. rubric:: Prerequisites
+
+- |intelex|
+- Scikit-learn
+- read and write permissions to Scikit-learn files
+
+Patch all supported algorithms
+===============================
+
+To patch all :ref:`supported algorithms <sklearn_algorithms>`, run::
+
+    python -m sklearnex.glob patch_sklearn
+
+Patch selected algorithms
+=========================
+
+If you want to patch only some algorithms, use ``--algorithm`` or ``-a`` keys
+with a list of algorithms to patch.
+
+For example, to patch only SVC and RandomForestClassifier estimators, run::
+
+    python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier
+
+Disable patching notifications
+==============================
+
+If you do not want to receive patching notifications, then use ``--no-verbose`` or ``-nv`` keys::
+
+    python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier -nv
+
+.. note::
+    If you run the global patching command several times with different parameters,
+    then only the last configuration will be applied.
+
+Disable global patching
+=======================
+
+To disable global patching, use the following command::
+
+    python -m sklearnex.glob unpatch_sklearn
+
+Enable global patching via code
+===============================
+
+You can also enable global patching in your code. To do this,
+use the ``patch_sklearn`` function with the ``global_patch`` argument::
+
+    from sklearnex import patch_sklearn
+    patch_sklearn(global_patch=True)
+    import sklearn
+
+After that, Scikit-learn patches will be enabled in the current application and
+in all others that use the same environment.
+
+Disable global patching via code
+================================
+
+To disable global patching via code, use the ``global_patch``
+argument in the ``unpatch_sklearn`` function::
+
+    from sklearnex import unpatch_sklearn
+    unpatch_sklearn(global_patch=True)
+
+.. note::
+    If you clone an environment with enabled global patching, it will already be applied in the new environment.
diff --git a/2024.3/_sources/guide/acceleration.rst.txt b/2024.3/_sources/guide/acceleration.rst.txt
index 952ee73539..2654a548a6 100644
--- a/2024.3/_sources/guide/acceleration.rst.txt
+++ b/2024.3/_sources/guide/acceleration.rst.txt
@@ -15,7 +15,7 @@
 .. *******************************************************************************/
 
 ########################
-Tuning Guide
+Performance Tuning Guide
 ########################
 
 The performance of some algorithms changes based on the parameters that are used.
diff --git a/2024.3/_sources/index.rst.txt b/2024.3/_sources/index.rst.txt
index 62055385c8..a7840e8266 100644
--- a/2024.3/_sources/index.rst.txt
+++ b/2024.3/_sources/index.rst.txt
@@ -23,82 +23,43 @@
 |intelex|
 #########
 
-Intel(R) Extension for Scikit-learn is a **free software AI accelerator** designed to deliver up to **100X** faster performance for your existing scikit-learn code.
-The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.
+With |intelex| you can accelerate your Scikit-learn applications and
+still have full conformance with all Scikit-Learn APIs and algorithms.
+|intelex| is a free software AI accelerator that brings over 10-100X acceleration across a variety of applications.
 
-.. rubric:: Designed for Data Scientists and Framework Designers
-
-
-Use Intel(R) Extension for Scikit-learn, to:
-
-* Speed up training and inference by up to 100x with the equivalent mathematical accuracy
-* Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs
-* Integrate the extension into your existing Scikit-learn applications without code modifications
-* Enable and disable the extension with a couple of lines of code or at the command line
-
-Intel(R) Extension for Scikit-learn is also a part of `Intel(R) AI Tools <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html>`_.
-
-
-.. image:: _static/scikit-learn-acceleration.PNG
-  :width: 800
-
-
-These performance charts use benchmarks that you can find in the `scikit-learn bench repository <https://github.com/IntelPython/scikit-learn_bench>`_. 
-
-
-Supported Algorithms 
----------------------
-
-See all of the :ref:`sklearn_algorithms`. 
+|intelex| offers you a way to accelerate existing scikit-learn code.
+The acceleration is achieved through :term:`patching`:
+replacing the stock scikit-learn algorithms with their optimized versions provided by the extension.
 
+.. rubric:: Designed for Data Scientists and Framework Designers
 
-Intel(R) Optimizations
-----------------------------------
-
-Enable Intel(R) CPU Optimizations
-*********************************
-
-::
-
-   import numpy as np
-   from sklearnex import patch_sklearn
-   patch_sklearn()
-
-   from sklearn.cluster import DBSCAN
-
-   X = np.array([[1., 2.], [2., 2.], [2., 3.],
-               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
-   clustering = DBSCAN(eps=3, min_samples=2).fit(X)
-
-Enable Intel(R) GPU optimizations
-*********************************
-::
-
-   import numpy as np
-   import dpctl
-   from sklearnex import patch_sklearn, config_context
-   patch_sklearn()
-
-   from sklearn.cluster import DBSCAN
+|intelex| was created to provide data scientists with a way to get a better performance
+while using the familiar scikit-learn package and getting the same results.
 
-   X = np.array([[1., 2.], [2., 2.], [2., 3.],
-               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
-   with config_context(target_offload="gpu:0"):
-       clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+Usage
+------
 
+.. include:: usage.rst
 
+Important Links
+--------------------
+- |intelex_repo|_
+- `Machine Learning Benchmarks <https://github.com/IntelPython/scikit-learn_bench>`_
+- `About Intel(R) oneAPI Data Analytics Library <https://github.com/oneapi-src/oneDAL>`_
+- `About daal4py <https://github.com/intel/scikit-learn-intelex/tree/main/daal4py>`_
 
 .. toctree::
    :caption: Get Started
+   :titlesonly: 
    :hidden:
-   :maxdepth: 3
+   :maxdepth: 1
 
    quick-start.rst
-   samples.rst
-   kaggle.rst
+   what-is-patching.rst
+   support.rst
    
 .. toctree::
-   :caption: Developer Guide
+   :caption: Features
    :hidden:
    :maxdepth: 2
 
@@ -106,29 +67,57 @@ Enable Intel(R) GPU optimizations
    oneAPI and GPU support <oneapi-gpu.rst>
    distributed-mode.rst
    verbose.rst
+
+.. toctree::
+   :caption: Releases
+   :hidden:
+   :maxdepth: 2
+
+   releases.rst
    deprecation.rst
-   
+
+.. toctree::
+   :caption: Installation Guide
+   :hidden:
+   :maxdepth: 2
+
+   installation.rst
+   System Requirements <system-requirements.rst>
+   memory-requirements.rst
+
+.. toctree::
+   :caption: Examples
+   :hidden:
+   :maxdepth: 2
+
+   samples.rst
+   kaggle.rst
 
 .. toctree::
    :caption: Performance
    :hidden:
    :maxdepth: 2
 
+   acceleration.rst
    guide/acceleration.rst
 
 .. toctree::
-   :caption: Learn 
+   :caption: Blogs & Publications
    :hidden:
    :maxdepth: 2
 
-   Tutorials & Case Studies <tutorials.rst>
    Medium Blogs <blogs.rst>
 
 .. toctree::
-   :caption: More
+   :caption: How to contribute
    :hidden:
    :maxdepth: 2
-   
-   Support <support.rst>
+
    contribute.rst
+
+.. toctree::
+   :caption: License
+   :hidden:
+   :maxdepth: 2
+
    license.rst
diff --git a/latest/_sources/installation.rst.txt b/2024.3/_sources/installation.rst.txt
similarity index 98%
rename from latest/_sources/installation.rst.txt
rename to 2024.3/_sources/installation.rst.txt
index be10e818bc..fe719c3855 100644
--- a/latest/_sources/installation.rst.txt
+++ b/2024.3/_sources/installation.rst.txt
@@ -136,7 +136,7 @@ Sources are available in |intelex_repo|_.
     daal4py >= 2021.4
 
 .. note::
-    You can `build daal4py from sources <https://github.com/intel/scikit-learn-intelex/blob/master/daal4py/INSTALL.md>`_ or get it from `distribution channels
+    You can `build daal4py from sources <https://github.com/intel/scikit-learn-intelex/blob/main/daal4py/INSTALL.md>`_ or get it from `distribution channels
     <https://intelpython.github.io/daal4py/#getting-daal4py>`_.
 
 Configure the build with environment variables
diff --git a/2024.3/_sources/kaggle.rst.txt b/2024.3/_sources/kaggle.rst.txt
index 364ab723d1..a00152a403 100644
--- a/2024.3/_sources/kaggle.rst.txt
+++ b/2024.3/_sources/kaggle.rst.txt
@@ -16,25 +16,21 @@
 
 .. _kaggle:
 
-####################
 Kaggle Kernels
-####################
+--------------------
 
-See Kaggle notebooks created by |intelex| developers.
+Check out Kaggle notebooks created by |intelex| developers.
 
-Acceleration
----------------------------------
+.. rubric:: Acceleration
 
 `Introduction to scikit-learn-intelex <https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook>`_
 provides a summary of the speedup you can achieve with |intelex|.
 
-Machine Learning Workflows
----------------------------------
+.. rubric:: Machine Learning Workflows
 
 Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.
 
 Kaggle kernels that use scikit-learn and |intelex|:
-******************************************************************
 
 .. toctree::
 
@@ -42,7 +38,6 @@ Kaggle kernels that use scikit-learn and |intelex|:
    Regression Tasks <kaggle/regression.rst>
 
 Kaggle kernels that use AutoML with |intelex|:
-******************************************************************
 
 .. toctree::
 
diff --git a/2024.3/_sources/memory-requirements.rst.txt b/2024.3/_sources/memory-requirements.rst.txt
new file mode 100644
index 0000000000..bfbd6f8e18
--- /dev/null
+++ b/2024.3/_sources/memory-requirements.rst.txt
@@ -0,0 +1,42 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _memory_requirements:
+
+###################
+Memory Requirements 
+###################
+
+By default, algorithms in |intelex| run in multi-thread mode, which uses all available threads. 
+This may lead to optimized algorithms consuming more RAM than the corresponding stock scikit-learn algorithms.
+
+.. list-table::
+   :widths: 10 30 30
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Single-thread mode
+     - Multi-thread mode
+   * - SVM
+     - Both Scikit-learn and |intelex| consume approximately the same amount of RAM.
+     - In |intelex|, an algorithm with ``N`` threads will consume ``N`` times more RAM.
+
+Memory consumption on GPU
+-------------------------
+In all |intelex| algorithms with GPU support, computations run on device memory. 
+The device memory must be large enough to store a copy of the entire dataset.
+Additional device memory may also be required for internal arrays used in computation.
diff --git a/2024.3/_sources/quick-start.rst.txt b/2024.3/_sources/quick-start.rst.txt
index 4a486e0b5a..a7b000c7dc 100644
--- a/2024.3/_sources/quick-start.rst.txt
+++ b/2024.3/_sources/quick-start.rst.txt
@@ -14,404 +14,17 @@
 .. * limitations under the License.
 .. *******************************************************************************/
 
-.. |intelex_repo| replace:: |intelex| repository
-.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
+.. _quick_start:
 
-####################
+############################################
 Quick Start
-####################
-
-Get ready to elevate your scikit-learn code with |intelex| and experience the benefits of accelerated performance in just a few simple steps. 
-
-Compatibility with Scikit-learn*
----------------------------------
-
-Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.
-
-Integrate |intelex|
---------------------
-
-Patching 
-**********************
-
-Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension. 
-This action is called ``patching``. This is not a permanent change so you can always undo the patching if necessary.
-
-To patch Intel® Extension for Scikit-learn, use one of these methods: 
-
-.. list-table:: 
-   :header-rows: 1
-   :align: left
-
-   * - Method
-     - Action
-   * - Use a flag in the command line
-     - Run this command:
-
-       :: 
-         
-          python -m sklearnex my_application.py
-   * - Modify your script 
-     - Add the following lines:
-
-       ::
- 
-          from sklearnex import patch_sklearn
-          patch_sklearn()   
-   * - Import an estimator from the ``sklearnex`` module
-     - Run this command:
-
-       ::
-
-          from sklearnex.neighbors import NearestNeighbors
-
-
-
-These patching methods are interchangeable.
-They support different enabling scenarios while producing the same result.
-
-   
-**Example**
-
-This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.
-  
-.. code-block:: python
-  :caption: Example: Drop-In Patching
-   
-    import numpy as np
-    from sklearnex import patch_sklearn
-    patch_sklearn()
-
-    # You need to re-import scikit-learn algorithms after the patch
-    from sklearn.cluster import KMeans
-  
-    # The use of the original Scikit-learn is not changed
-    X = np.array([[1,  2], [1,  4], [1,  0],
-                [10, 2], [10, 4], [10, 0]])
-    kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
-    print(f"kmeans.labels_ = {kmeans.labels_}")
-
-
-Global Patching
-**********************
-
-You can also use global patching to patch all your scikit-learn applications without any additional actions.
-
-Before you begin, make sure that you have read and write permissions for Scikit-learn files. 
-
-With global patching, you can:
-
-.. list-table:: 
-   :header-rows: 1
-   :align: left
-
-   * - Task
-     - Action
-     - Note
-   * - Patch all supported algorithms
-     - Run this command:
-
-       :: 
-         
-          python -m sklearnex.glob patch_sklearn
-     
-     - If you run the global patching command several times with different parameters, then only the last configuration is applied.
-   * - Patch selected algorithms
-     - Use ``--algorithm`` or ``-a`` keys with a list of algorithms to patch. For example, to patch only ``SVC`` and ``RandomForestClassifier`` estimators, run
-
-       ::
- 
-           python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier
-  
-     -   
-   * - Enable global patching via code
-     - Use the ``patch_sklearn`` function with the ``global_patch`` argument:
-
-       ::
-
-          from sklearnex import patch_sklearn
-          patch_sklearn(global_patch=True)
-          import sklearn
-      
-     - After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.
-   * - Disable patching notifications
-     - Use ``--no-verbose`` or ``-nv`` keys:
-
-       ::
-
-          python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier -nv
-     -  
-   * - Disable global patching
-     - Run this command:
-
-       ::
-
-          python -m sklearnex.glob unpatch_sklearn
-     -
-   * - Disable global patching via code
-     - Use the ``global_patch`` argument in the ``unpatch_sklearn`` function
-
-       ::
-
-          from sklearnex import unpatch_sklearn
-          unpatch_sklearn(global_patch=True)
-     -
-    
-.. tip:: If you clone an environment with enabled global patching, it will already be applied in the new environment.
-
-Unpatching
-**********************
-
-To undo the patch (also called `unpatching`) is to return scikit-learn to original implementation and
-replace patched algorithms with the stock scikit-learn algorithms.
-
-To unpatch successfully, you must reimport the scikit-learn package::
-
-  sklearnex.unpatch_sklearn()
-  # Re-import scikit-learn algorithms after the unpatch
-  from sklearn.cluster import KMeans  
-
-
-Installation 
---------------------
-
-.. contents:: :local:
-
-.. tip:: To prevent version conflicts, we recommend creating and activating a new environment for |intelex|. 
-
-Install from PyPI 
-**********************
-
-Recommended by default. 
-
-To install |intelex|, run:
-
-::
-
-  pip install scikit-learn-intelex
-
-**Supported Configurations**
-
-.. list-table::
-   :header-rows: 1
-   :align: left
-
-   * - OS / Python version
-     - Python 3.8
-     - Python 3.9
-     - Python 3.10
-     - Python 3.11
-     - Python 3.12
-   * - Linux* OS
-     - [CPU, GPU]
-     - [CPU, GPU]
-     - [CPU, GPU]
-     - [CPU, GPU]
-     - [CPU, GPU]
-   * - Windows* OS
-     - [CPU, GPU]
-     - [CPU, GPU]
-     - [CPU, GPU]
-     - [CPU, GPU]
-     - [CPU, GPU]
-
-
-
-Install from Anaconda* Cloud
-********************************************
-
-To prevent version conflicts, we recommend installing `scikit-learn-intelex` into a new conda environment.
-
-.. tabs::
-
-   .. tab:: Conda-Forge channel
-
-      Recommended by default. 
-      
-      To install, run::
-
-        conda install scikit-learn-intelex -c conda-forge
-      
-      .. list-table:: **Supported Configurations**
-         :header-rows: 1
-         :align: left
-
-         * - OS / Python version
-           - Python 3.8
-           - Python 3.9
-           - Python 3.10
-           - Python 3.11
-           - Python 3.12
-         * - Linux* OS
-           - [CPU]
-           - [CPU]
-           - [CPU]
-           - [CPU]
-           - [CPU]
-         * - Windows* OS
-           - [CPU]
-           - [CPU]
-           - [CPU]
-           - [CPU]
-           - [CPU]
-
-
-   .. tab:: Intel channel
-
-      Recommended for the Intel® Distribution for Python users. 
-
-      To install, run::
-
-        conda install scikit-learn-intelex -c intel
-      
-      .. list-table:: **Supported Configurations**
-         :header-rows: 1
-         :align: left
-
-         * - OS / Python version
-           - Python 3.8
-           - Python 3.9
-           - Python 3.10
-           - Python 3.11
-           - Python 3.12
-         * - Linux* OS
-           - [CPU, GPU]
-           - [CPU, GPU]
-           - [CPU, GPU]
-           - [CPU, GPU]
-           - [CPU, GPU]
-         * - Windows* OS
-           - [CPU, GPU]
-           - [CPU, GPU]
-           - [CPU, GPU]
-           - [CPU, GPU]
-           - [CPU, GPU]
- 
-
-
-   .. tab:: Main channel
-
-      To install, run::
-
-        conda install scikit-learn-intelex
-      
-      .. list-table:: **Supported Configurations**
-         :header-rows: 1
-         :align: left
-
-         * - OS / Python version
-           - Python 3.8
-           - Python 3.9
-           - Python 3.10
-           - Python 3.11
-           - Python 3.12
-         * - Linux* OS
-           - [CPU]
-           - [CPU]
-           - [CPU]
-           - [CPU]
-           - [CPU]
-         * - Windows* OS
-           - [CPU]
-           - [CPU]
-           - [CPU]
-           - [CPU]
-           - [CPU]
-
-
-
-Build from Sources
-**********************
-
-See `Installation instructions <https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md>`_ to build |intelex| from the sources.
-
-Install Intel*(R) AI Tools
-****************************
-
-Download the Intel AI Tools `here <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html>`_. The extension is already included.
-
-Release Notes
--------------------
-
-See the `Release Notes <https://github.com/intel/scikit-learn-intelex/releases>`_ for each version of Intel® Extension for Scikit-learn*.  
-
-System Requirements 
---------------------
-
-Hardware Requirements
-**********************
-
-.. tabs::
-
-   .. tab:: CPU
-
-      All processors with ``x86`` architecture with at least one of the following instruction sets:
-
-        - SSE2
-        - SSE4.2
-        - AVX2
-        - AVX512
-       
-      .. note:: ARM* architecture is not supported.
-
-   .. tab:: GPU
-
-      - All Intel® integrated and discrete GPUs
-      - Intel® GPU drivers
-
-
-.. tip:: Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our :ref:`blogs <blogs>`.
-
-
-Software Requirements
-**********************
-
-.. tabs::
-
-   .. tab:: CPU
-
-      - Linux* OS: Ubuntu* 18.04 or newer
-      - Windows* OS 10 or newer
-      - Windows* Server 2019 or newer
-
-   .. tab:: GPU
-
-      - Linux* OS: Ubuntu* 18.04 or newer
-      - Windows* OS 10 or newer
-      - Windows* Server 2019 or newer
-      
-      .. important::
-         
-         If you use accelerators, refer to `oneAPI DPC++/C++ Compiler System Requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_.
-
-Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:
-
-* 1.0.X
-* 1.1.X
-* 1.2.X 
-* 1.3.X
-
-Memory Requirements
-**********************
-By default, algorithms in |intelex| run in the multi-thread mode. This mode uses all available threads. 
-Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.
-
-.. list-table::
-   :header-rows: 1
-   :align: left
-
-   * - Algorithm
-     - Single-thread mode
-     - Multi-thread mode
-   * - SVM
-     - Both Scikit-learn and |intelex| consume approximately the same amount of RAM.
-     - In |intelex|, an algorithm with ``N`` threads consumes ``N`` times more RAM.
-
-In all |intelex| algorithms with GPU support, computations run on device memory. 
-The device memory must be large enough to store a copy of the entire dataset.
-You may also require additional device memory for internal arrays that are used in computation.
+############################################
 
+.. include:: usage.rst
 
 .. seealso::
 
-   :ref:`Samples<samples>`
+   - :ref:`Supported algorithms and parameters <sklearn_algorithms>`
+   - :ref:`samples`
+   - :ref:`global_patching`
+   - `Getting Started Guide <https://www.intel.com/content/www/us/en/developer/articles/guide/intel-extension-for-scikit-learn-getting-started.html>`_
diff --git a/2023.2/_sources/releases.rst.txt b/2024.3/_sources/releases.rst.txt
similarity index 100%
rename from 2023.2/_sources/releases.rst.txt
rename to 2024.3/_sources/releases.rst.txt
diff --git a/2024.3/_sources/samples.rst.txt b/2024.3/_sources/samples.rst.txt
index e616e8f2d9..f649329113 100644
--- a/2024.3/_sources/samples.rst.txt
+++ b/2024.3/_sources/samples.rst.txt
@@ -17,7 +17,7 @@
 .. _samples:
 
 #######
-Samples 
+Samples
 #######
 
 The following samples are also provided as Jupyter notebooks in |intelex| repository.
@@ -65,4 +65,4 @@ If you want to run them locally, refer to `these instructions
 
    There are also :ref:`Kaggle kernels <kaggle>` that use |intelex| for a variety of machine learning scenarios.
 
-.. note:: Explore the complete list of oneAPI code samples in the `oneAPI Samples Catalog <https://oneapi-src.github.io/oneAPI-samples/>`_. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.
+.. note:: Explore the complete list of oneAPI code samples in the `oneAPI Samples Catalog <https://oneapi-src.github.io/oneAPI-samples/>`_. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.
\ No newline at end of file
diff --git a/2024.3/_sources/support.rst.txt b/2024.3/_sources/support.rst.txt
index 03a2dddadb..15d495c864 100644
--- a/2024.3/_sources/support.rst.txt
+++ b/2024.3/_sources/support.rst.txt
@@ -14,21 +14,12 @@
 .. * limitations under the License.
 .. *******************************************************************************/
 
-######################################################
-Intel(R) Extension for Scikit-learn Support
-######################################################
+Support
+--------------------
+Report issues, ask questions, and provide suggestions using:
 
+- `GitHub Issues <https://github.com/intel/scikit-learn-intelex/issues>`_
+- `GitHub Discussions <https://github.com/intel/scikit-learn-intelex/discussions>`_
+- `Forum <https://community.intel.com/t5/Intel-Distribution-for-Python/bd-p/distribution-python>`_
 
-We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn. 
-
-Use the following methods if you face any challenges. 
-
-
-Issues
-----------------------------------
-
-If you have a problem, check out the `GitHub Issues <https://github.com/intel/scikit-learn-intelex/issues>`_ to see if the issue you want to address is already reported. 
-
-You may find users that have encountered the same bug or have similar ideas for changes or updates.
-
-You can use issues to report a problem, make a feature request, or add comments on an existing issue.
+You may reach out to project maintainers privately at onedal.maintainers@intel.com
diff --git a/2024.3/_sources/system-requirements.rst.txt b/2024.3/_sources/system-requirements.rst.txt
new file mode 100644
index 0000000000..a6216e1195
--- /dev/null
+++ b/2024.3/_sources/system-requirements.rst.txt
@@ -0,0 +1,213 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _system_requirements:
+
+#################################################
+System requirements and supported configurations
+#################################################
+
+This page provides details about hardware, operating system, and software prerequisites for |intelex|.
+
+Supported configurations
+------------------------
+
+|intelex| supports optimizations for the last four versions of scikit-learn.
+The latest release of scikit-learn-intelex-2023.1.X supports scikit-learn 0.24.X, 1.0.X, 1.1.X and 1.2.X.
+
+|intelex| is available for installation from different channels. 
+There is a difference in supported configurations for each distribution channel.
+
+.. _sys_req_pip:
+
+PyPI channel
+=============
+
+.. list-table::
+   :widths: 25 8 8 8 8 8
+   :header-rows: 1
+   :align: left
+
+   * - OS / Python version
+     - Python 3.7
+     - Python 3.8
+     - Python 3.9
+     - Python 3.10
+     - Python 3.11
+   * - Linux
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+   * - Windows
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+   * - macOS
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+
+.. _sys_req_conda_forge:
+
+Anaconda Cloud: Conda-Forge channel
+===================================
+
+.. list-table::
+   :widths: 25 8 8 8 8
+   :header-rows: 1
+   :align: left
+
+   * - OS / Python version
+     - Python 3.8
+     - Python 3.9
+     - Python 3.10
+     - Python 3.11
+   * - Linux
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+   * - Windows
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+   * - macOS
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+
+.. _sys_req_conda_intel:
+
+Anaconda Cloud: Intel channel
+==============================
+
+.. list-table::
+   :widths: 25 8 8 8 8 8
+   :header-rows: 1
+   :align: left
+
+   * - OS / Python version
+     - Python 3.7
+     - Python 3.8
+     - Python 3.9
+     - Python 3.10
+     - Python 3.11
+   * - Linux
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+   * - Windows
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+   * - macOS
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+
+.. _sys_req_conda_main:
+
+Anaconda Cloud: Main channel
+==============================
+
+.. list-table::
+   :widths: 25 8 8 8 8
+   :header-rows: 1
+   :align: left
+
+   * - OS / Python version
+     - Python 3.8
+     - Python 3.9
+     - Python 3.10
+     - Python 3.11
+   * - Linux
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+   * - Windows
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+   * - macOS
+     - [CPU]
+     - [CPU]
+     - [CPU]
+     - [CPU]
+
+System Requirements
+-------------------
+
+For CPU
+=======
+
+.. rubric:: Operating systems
+
+The last two versions of the following operating systems:
+
+- **Linux**\*
+- **Windows**\* and **Windows\* Server**
+- **macOS**\*
+
+.. rubric:: Hardware platforms
+
+- All processors with x86 architecture
+
+.. note::
+    Your processor must support at least one of SSE2, SSE4.2, AVX2, AVX512 instruction sets.
+
+.. note::
+    ARM* architecture is not supported.
+
+.. note::
+    Intel® processors provide better performance then other CPUs.
+    Read more about hardware comparison in our :ref:`blogs <blogs>`.
+
+For GPU
+=======
+
+.. important::
+    If you are using accelerators, please refer to the DPC++ compiler system
+    requirements `here <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_
+
+.. rubric:: Operating systems
+
+The last two versions of the following operating systems:
+
+- **Linux**\*
+- **Windows**\* and **Windows\* Server**
+
+.. rubric:: Hardware platforms
+
+- All Intel® integrated and discrete GPUs
+- `Intel® GPU drivers <https://www.intel.com/content/www/us/en/develop/articles/installation-guide-for-intel-oneapi-toolkits.html#installGPUdriver>`__
+
+.. seealso:: :ref:`oneapi_gpu`
diff --git a/2024.3/_sources/verbose.rst.txt b/2024.3/_sources/verbose.rst.txt
index cf77cc0669..1eee3c6e4b 100644
--- a/2024.3/_sources/verbose.rst.txt
+++ b/2024.3/_sources/verbose.rst.txt
@@ -27,11 +27,11 @@ Use |intelex| in verbose mode to find out which implementation of the algorithm
 
 To enable verbose mode, set the ``SKLEARNEX_VERBOSE`` environment variable as shown below:
 
-- On Linux* OS ::
+- On Linux and MacOS::
 
      export SKLEARNEX_VERBOSE=INFO
 
-- On Windows* OS ::
+- On Windows::
 
      set SKLEARNEX_VERBOSE=INFO
 
diff --git a/latest/_sources/what-is-patching.rst.txt b/2024.3/_sources/what-is-patching.rst.txt
similarity index 100%
rename from latest/_sources/what-is-patching.rst.txt
rename to 2024.3/_sources/what-is-patching.rst.txt
diff --git a/2024.3/_static/css/badge_only.css b/2024.3/_static/css/badge_only.css
index c718cee441..88ba55b965 100644
--- a/2024.3/_static/css/badge_only.css
+++ b/2024.3/_static/css/badge_only.css
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/2024.3/_static/css/theme.css b/2024.3/_static/css/theme.css
index 19a446a0e7..0f14f10646 100644
--- a/2024.3/_static/css/theme.css
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diff --git a/2024.3/_static/js/versions.js b/2024.3/_static/js/versions.js
new file mode 100644
index 0000000000..818bc9969f
--- /dev/null
+++ b/2024.3/_static/js/versions.js
@@ -0,0 +1,224 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = "True";
+const themeLanguageSelector = "True";
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${config.projects.translations
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/2024.3/_static/version_switcher.js b/2024.3/_static/version_switcher.js
index a2543fdb9c..162851d217 100644
--- a/2024.3/_static/version_switcher.js
+++ b/2024.3/_static/version_switcher.js
@@ -1,36 +1,31 @@
-fetch("https://aepanchi.github.io/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=>
-    load_versions(respText));
-
-
-function load_versions(json){
-    var button = document.getElementById('version-switcher-button')
-    var container = document.getElementById('version-switcher-dropdown')
-    var loc = window.location.href;
-    var s = document.createElement('select');
-    s.style = "border-radius:5px;"
-    const versions = JSON.parse(json);
-    for (entry of versions){
-        var o = document.createElement('option');
-        var optionText = '';
-        if ('name' in entry){
-            optionText = entry.name;
-        }else{
-            optionText = entry.version;
-        }
-        o.value = entry.url;
-        if (current_version == entry.version){
-            o.selected = true;
-            optionText = optionText;
-        }
-        o.innerHTML = optionText;
-        s.append(o);
-    }
-    s.addEventListener("change", (event)=> {
-        var current_url = new URL(window.location.href);
-        var path = current_url.pathname;
-        //strip version from path
-        var page_path = path.substring(project_name.length+current_version.length+3);
-        window.location.href = s.value + page_path;
-    });
-    container.append(s);
-}
\ No newline at end of file
+function load_versions(json){
+    var button = document.getElementById('version-switcher-button')
+    var container = document.getElementById('version-switcher-dropdown')
+    var loc = window.location.href;
+    var s = document.createElement('select');
+    s.style = "border-radius:5px;"
+    const versions = JSON.parse(json);
+    for (entry of versions){
+        var o = document.createElement('option');
+        var optionText = '';
+        if ('name' in entry){
+            optionText = entry.name;
+        }else{
+            optionText = entry.version;
+        }
+        o.value = entry.url;
+        if (current_version == entry.version){
+            o.selected = true;
+        }
+        o.innerHTML = optionText;
+        s.append(o);
+    }
+    s.addEventListener("change", ()=> {
+        var current_url = new URL(window.location.href);
+        var path = current_url.pathname;
+        //strip version from path
+        var page_path = path.substring(project_name.length+current_version.length+3);
+        window.location.href = s.value + page_path;
+    });
+    container.append(s);
+}
diff --git a/latest/acceleration.html b/2024.3/acceleration.html
similarity index 78%
rename from latest/acceleration.html
rename to 2024.3/acceleration.html
index 36cf78f210..2436ac800c 100644
--- a/latest/acceleration.html
+++ b/2024.3/acceleration.html
@@ -1,23 +1,26 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Acceleration &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="_static/custom.css" type="text/css" />
-      <link rel="stylesheet" href="_static/tabs.css" type="text/css" />
-    <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Acceleration &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
+
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -25,6 +28,7 @@
     <link rel="prev" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" href="kaggle/automl.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -51,10 +55,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,6 +63,30 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.3"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
@@ -116,6 +140,7 @@
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -179,7 +204,7 @@ <h3>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Permalink
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b04bb190>
+<jinja2.runtime.BlockReference object at 0x7fca67ad9410>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -204,6 +229,28 @@ <h3>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Permalink
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.3"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2024.3/algorithms.html b/2024.3/algorithms.html
index 703d7b701e..ee7363e0f5 100644
--- a/2024.3/algorithms.html
+++ b/2024.3/algorithms.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -7,34 +9,26 @@
   <title>Supported Algorithms &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
       <link rel="stylesheet" type="text/css" href="_static/custom.css" />
       <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
-      <link rel="stylesheet" type="text/css" href="_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css" />
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/version_switcher.js"></script>
-        <script src="_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" href="oneapi-gpu.html" />
-    <link rel="prev" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" href="kaggle/automl.html" />
+    <link rel="prev" title="Support" href="support.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -61,9 +55,6 @@
           <a href="index.html" class="icon icon-home">
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@@ -95,138 +86,14 @@
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
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 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
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-</li>
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-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Supported Algorithms</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#on-cpu">on CPU</a><ul>
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-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
-</ul>
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+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
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+    <link rel="next" title="How to Contribute" href="contribute.html" />
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -61,9 +55,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,205 +86,51 @@
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   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
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-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
-</ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 </ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
 <li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
-</ul>
-</li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
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-<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
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@@ -346,14 +183,14 @@
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-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
-</ul>
-</li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">How to Contribute</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#licensing">Licensing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#pull-requests">Pull Requests</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#before-contributing-changes">Before Contributing Changes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#issues">Issues</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#pull-requests">Pull Requests</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="#code-style">Code Style</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
@@ -324,40 +165,39 @@
              
   <section id="how-to-contribute">
 <h1>How to Contribute<a class="headerlink" href="#how-to-contribute" title="Permalink to this heading"></a></h1>
-<p>As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn.
-This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.</p>
-<section id="licensing">
-<h2>Licensing<a class="headerlink" href="#licensing" title="Permalink to this heading"></a></h2>
-<p>Intel(R) Extension for Scikit-learn uses the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE">Apache 2.0 License</a>. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.</p>
+<p>We welcome community contributions to Intel® Extension for Scikit-learn*. You can:</p>
+<ul class="simple">
+<li><p>Submit your changes directly with a <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/pulls">pull request</a>.</p></li>
+<li><p>Log a bug or make a feature request with an <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">issue</a>.</p></li>
+</ul>
+<p>Refer to our guidelines on pull requests and issues before you proceed.</p>
+<section id="issues">
+<h2>Issues<a class="headerlink" href="#issues" title="Permalink to this heading"></a></h2>
+<p>Use <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">GitHub issues</a> to:</p>
+<ul class="simple">
+<li><p>report an issue</p></li>
+<li><p>make a feature request</p></li>
+</ul>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To report a vulnerability, refer to
+<a class="reference external" href="https://www.intel.com/content/www/us/en/security-center/default.html">Intel vulnerability reporting policy</a>.</p>
+</div>
 </section>
 <section id="pull-requests">
 <h2>Pull Requests<a class="headerlink" href="#pull-requests" title="Permalink to this heading"></a></h2>
-<p>No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.</p>
-<p>Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">name</span> <span class="s2">&quot;Kate Developer&quot;</span>
-<span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">email</span> <span class="n">kate</span><span class="o">.</span><span class="n">developer</span><span class="nd">@company</span><span class="o">.</span><span class="n">com</span>
-</pre></div>
-</div>
-<section id="before-contributing-changes">
-<h3>Before Contributing Changes<a class="headerlink" href="#before-contributing-changes" title="Permalink to this heading"></a></h3>
-<p>Make sure you can build the product and run all the tests with your patch.
-For a larger feature, provide a relevant test.
-Document your code. Our project uses reStructuredText for documentation.
-For new file(s), specify the appropriate copyright year in the first line.
-Submit a pull request into the master branch.
-Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.</p>
-</section>
-</section>
-<section id="code-style">
-<h2>Code Style<a class="headerlink" href="#code-style" title="Permalink to this heading"></a></h2>
-<p>We use <a class="reference external" href="https://black.readthedocs.io/en/stable/">black</a> and <a class="reference external" href="https://pycqa.github.io/isort/">isort</a> formatters for Python* code.
-The line length is 90 characters; use default options otherwise. You can find the linter configuration in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml">.pyproject.toml</a>.</p>
-<p>A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.</p>
-<p>Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pre</span><span class="o">-</span><span class="n">commit</span>
-<span class="n">pre</span><span class="o">-</span><span class="n">commit</span> <span class="n">install</span>
-</pre></div>
-</div>
+<p>To contribute your changes directly to the repository, do the following:</p>
+<ul class="simple">
+<li><p>Make sure you can build the product and run all the examples with your patch.</p></li>
+<li><p>For a larger feature, provide a relevant example.</p></li>
+<li><p>Document your code.</p></li>
+<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/pulls">Submit</a> a pull request into the <cite>main</cite> branch.
+Provide a brief description of the changes you are contributing.</p></li>
+</ul>
+<p>Public CI is enabled for the repository. Your PR should pass all of our checks.
+We will review your contribution and, if any additional fixes or modifications are necessary,
+we may give some feedback to guide you.
+When accepted, your pull request will be merged into our GitHub repository.</p>
 </section>
 </section>
 
@@ -365,14 +205,14 @@ <h2>Code Style<a class="headerlink" href="#code-style" title="Permalink to this
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="support.html" class="btn btn-neutral float-left" title="Intel(R) Extension for Scikit-learn Support" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="blogs.html" class="btn btn-neutral float-left" title="Follow us on Medium" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
         <a href="license.html" class="btn btn-neutral float-right" title="License" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f1245daf2b0>
+<jinja2.runtime.BlockReference object at 0x7fca6715d590>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -418,7 +258,8 @@ <h2>Code Style<a class="headerlink" href="#code-style" title="Permalink to this
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.3/deprecation.html b/2024.3/deprecation.html
index b185591054..532fc9ed48 100644
--- a/2024.3/deprecation.html
+++ b/2024.3/deprecation.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -7,34 +9,26 @@
   <title>Deprecation Notice &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
       <link rel="stylesheet" type="text/css" href="_static/custom.css" />
       <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
-      <link rel="stylesheet" type="text/css" href="_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css" />
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
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-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
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-        <script src="_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Tuning Guide" href="guide/acceleration.html" />
-    <link rel="prev" title="Verbose Mode" href="verbose.html" />
+    <link rel="next" title="Installation" href="installation.html" />
+    <link rel="prev" title="Release Notes" href="releases.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -61,9 +55,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,205 +86,54 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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             Intel(R) Extension for Scikit-learn*
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   </div>
   
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-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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 <ul class="current">
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+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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 <ul>
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 <ul>
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 </ul>
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+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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             Intel(R) Extension for Scikit-learn*
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@@ -93,205 +84,51 @@
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 <ul>
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 <ul>
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+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
@@ -108,6 +132,7 @@
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -211,7 +236,7 @@ <h2>Disable global patching via code<a class="headerlink" href="#disable-global-
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0923fa0>
+<jinja2.runtime.BlockReference object at 0x7fca6714df10>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -236,6 +261,28 @@ <h2>Disable global patching via code<a class="headerlink" href="#disable-global-
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.3"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2024.3/guide/acceleration.html b/2024.3/guide/acceleration.html
index d48935d00e..451965a95f 100644
--- a/2024.3/guide/acceleration.html
+++ b/2024.3/guide/acceleration.html
@@ -1,40 +1,34 @@
+
+
 <!DOCTYPE html>
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-  <title>Tuning Guide &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
+  <title>Performance Tuning Guide &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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+    <link rel="next" title="Follow us on Medium" href="../blogs.html" />
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -61,9 +55,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,205 +86,55 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
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-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 </ul>
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-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
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-</li>
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-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
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-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
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-  <section id="tuning-guide">
-<h1>Tuning Guide<a class="headerlink" href="#tuning-guide" title="Permalink to this heading"></a></h1>
+  <section id="performance-tuning-guide">
+<h1>Performance Tuning Guide<a class="headerlink" href="#performance-tuning-guide" title="Permalink to this heading"></a></h1>
 <p>The performance of some algorithms changes based on the parameters that are used.
 This section denotes the details of such cases.</p>
 <p>Refer to <a class="reference internal" href="../algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a> to see the full list of algorithms, parameters, and data formats supported in Intel® Extension for Scikit-learn*.</p>
@@ -402,14 +243,14 @@ <h1>Tuning Guide<a class="headerlink" href="#tuning-guide" title="Permalink to t
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+++ b/2024.3/index.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
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   <title>Intel® Extension for Scikit-learn* &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
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@@ -60,9 +54,6 @@
           <a href="#" class="icon icon-home">
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@@ -94,205 +85,51 @@
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
-</ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 </ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
 <li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
@@ -323,54 +160,108 @@
              
   <section id="intelex">
 <span id="index"></span><h1>Intel® Extension for Scikit-learn*<a class="headerlink" href="#intelex" title="Permalink to this heading"></a></h1>
-<p>Intel(R) Extension for Scikit-learn is a <strong>free software AI accelerator</strong> designed to deliver up to <strong>100X</strong> faster performance for your existing scikit-learn code.
-The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.</p>
+<p>With Intel® Extension for Scikit-learn* you can accelerate your Scikit-learn applications and
+still have full conformance with all Scikit-Learn APIs and algorithms.
+Intel® Extension for Scikit-learn* is a free software AI accelerator that brings over 10-100X acceleration across a variety of applications.</p>
+<p>Intel® Extension for Scikit-learn* offers you a way to accelerate existing scikit-learn code.
+The acceleration is achieved through <a class="reference internal" href="what-is-patching.html#term-patching"><span class="xref std std-term">patching</span></a>:
+replacing the stock scikit-learn algorithms with their optimized versions provided by the extension.</p>
 <p class="rubric">Designed for Data Scientists and Framework Designers</p>
-<p>Use Intel(R) Extension for Scikit-learn, to:</p>
-<ul class="simple">
-<li><p>Speed up training and inference by up to 100x with the equivalent mathematical accuracy</p></li>
-<li><p>Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs</p></li>
-<li><p>Integrate the extension into your existing Scikit-learn applications without code modifications</p></li>
-<li><p>Enable and disable the extension with a couple of lines of code or at the command line</p></li>
+<p>Intel® Extension for Scikit-learn* was created to provide data scientists with a way to get a better performance
+while using the familiar scikit-learn package and getting the same results.</p>
+<section id="usage">
+<h2>Usage<a class="headerlink" href="#usage" title="Permalink to this heading"></a></h2>
+<p>You may enable patching in different ways:</p>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<p>These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.</p>
+</div>
+<ul>
+<li><p>Without editing the code of a scikit-learn application by using the following command line flag:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span> <span class="n">my_application</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+</li>
+<li><p>Directly from the script:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</li>
 </ul>
-<p>Intel(R) Extension for Scikit-learn is also a part of <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html">Intel(R) AI Tools</a>.</p>
-<a class="reference internal image-reference" href="_images/scikit-learn-acceleration.PNG"><img alt="_images/scikit-learn-acceleration.PNG" src="_images/scikit-learn-acceleration.PNG" style="width: 800px;" /></a>
-<p>These performance charts use benchmarks that you can find in the <a class="reference external" href="https://github.com/IntelPython/scikit-learn_bench">scikit-learn bench repository</a>.</p>
-<section id="supported-algorithms">
-<h2>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Permalink to this heading"></a></h2>
-<p>See all of the <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a>.</p>
-</section>
-<section id="intel-r-optimizations">
-<h2>Intel(R) Optimizations<a class="headerlink" href="#intel-r-optimizations" title="Permalink to this heading"></a></h2>
-<section id="enable-intel-r-cpu-optimizations">
-<h3>Enable Intel(R) CPU Optimizations<a class="headerlink" href="#enable-intel-r-cpu-optimizations" title="Permalink to this heading"></a></h3>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<p>You have to import scikit-learn <strong>after</strong> these lines. Otherwise, the patching will not affect the original scikit-learn estimators.</p>
+</div>
+<ul>
+<li><p>Through importing the desired estimator from the sklearnex module in your script:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
+</pre></div>
+</div>
+</li>
+<li><p>Through <a class="reference internal" href="what-is-patching.html#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
+</pre></div>
+</div>
+</li>
+</ul>
+<p class="rubric">Example</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
 <span class="n">patch_sklearn</span><span class="p">()</span>
 
-<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+<span class="c1"># You need to re-import scikit-learn algorithms after the patch</span>
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
 
-<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
-            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">0</span><span class="p">],</span>
+              <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
+<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;kmeans.labels_ = </span><span class="si">{</span><span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-</section>
-<section id="enable-intel-r-gpu-optimizations">
-<h3>Enable Intel(R) GPU optimizations<a class="headerlink" href="#enable-intel-r-gpu-optimizations" title="Permalink to this heading"></a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">dpctl</span>
-<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span><span class="p">,</span> <span class="n">config_context</span>
-<span class="n">patch_sklearn</span><span class="p">()</span>
-
-<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
-
-<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
-            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">target_offload</span><span class="o">=</span><span class="s2">&quot;gpu:0&quot;</span><span class="p">):</span>
-    <span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<p>In the example above, you can see that the use of the original Scikit-learn
+has not changed. This behavior is achieved through drop-in patching.</p>
+<p>To <a class="reference internal" href="what-is-patching.html#term-unpatching"><span class="xref std std-term">undo the patch</span></a>, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sklearnex</span><span class="o">.</span><span class="n">unpatch_sklearn</span><span class="p">()</span>
+<span class="c1"># Re-import scikit-learn algorithms after the unpatch:</span>
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
 </pre></div>
 </div>
+<p>You may specify which algorithms to patch:</p>
+<ul>
+<li><p>Patching only one algorithm:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="c1"># The names match scikit-learn estimators</span>
+<span class="n">patch_sklearn</span><span class="p">(</span><span class="s2">&quot;SVC&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</li>
+<li><p>Patching several algorithms:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="c1"># The names match scikit-learn estimators</span>
+<span class="n">patch_sklearn</span><span class="p">([</span><span class="s2">&quot;SVC&quot;</span><span class="p">,</span> <span class="s2">&quot;DBSCAN&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</li>
+</ul>
+</section>
+<section id="important-links">
+<h2>Important Links<a class="headerlink" href="#important-links" title="Permalink to this heading"></a></h2>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex">Intel® Extension for Scikit-learn* repository</a></p></li>
+<li><p><a class="reference external" href="https://github.com/IntelPython/scikit-learn_bench">Machine Learning Benchmarks</a></p></li>
+<li><p><a class="reference external" href="https://github.com/oneapi-src/oneDAL">About Intel(R) oneAPI Data Analytics Library</a></p></li>
+<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/daal4py">About daal4py</a></p></li>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+<div class="toctree-wrapper compound">
+</div>
+<div class="toctree-wrapper compound">
+</div>
+<div class="toctree-wrapper compound">
+</div>
 <div class="toctree-wrapper compound">
 </div>
 <div class="toctree-wrapper compound">
@@ -381,7 +272,6 @@ <h3>Enable Intel(R) GPU optimizations<a class="headerlink" href="#enable-intel-r
 </div>
 <div class="toctree-wrapper compound">
 </div>
-</section>
 </section>
 </section>
 
@@ -395,7 +285,7 @@ <h3>Enable Intel(R) GPU optimizations<a class="headerlink" href="#enable-intel-r
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f124596a7d0>
+<jinja2.runtime.BlockReference object at 0x7fca671fa550>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -441,7 +331,8 @@ <h3>Enable Intel(R) GPU optimizations<a class="headerlink" href="#enable-intel-r
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/latest/installation.html b/2024.3/installation.html
similarity index 88%
rename from latest/installation.html
rename to 2024.3/installation.html
index 7afaae97a8..ebdac58360 100644
--- a/latest/installation.html
+++ b/2024.3/installation.html
@@ -1,24 +1,27 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Installation &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="_static/custom.css" type="text/css" />
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-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Installation &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
+
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/tabs.js"></script>
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -26,6 +29,7 @@
     <link rel="prev" title="Deprecation Notice" href="deprecation.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -52,10 +56,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -64,6 +64,30 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.3"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
@@ -124,6 +148,7 @@
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -268,7 +293,7 @@ <h3><a class="toc-backref" href="#id3" role="doc-backlink">Install from Anaconda
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>You can <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/daal4py/INSTALL.md">build daal4py from sources</a> or get it from <a class="reference external" href="https://intelpython.github.io/daal4py/#getting-daal4py">distribution channels</a>.</p>
+<p>You can <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/daal4py/INSTALL.md">build daal4py from sources</a> or get it from <a class="reference external" href="https://intelpython.github.io/daal4py/#getting-daal4py">distribution channels</a>.</p>
 </div>
 <section id="configure-the-build-with-environment-variables">
 <h3>Configure the build with environment variables<a class="headerlink" href="#configure-the-build-with-environment-variables" title="Permalink to this heading"></a></h3>
@@ -331,7 +356,7 @@ <h3>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b046af10>
+<jinja2.runtime.BlockReference object at 0x7fca66f46550>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -356,6 +381,28 @@ <h3>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.3"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/2024.3/kaggle.html b/2024.3/kaggle.html
index d3846a4fb2..2eba21ed5b 100644
--- a/2024.3/kaggle.html
+++ b/2024.3/kaggle.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -7,27 +9,18 @@
   <title>Kaggle Kernels &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
       <link rel="stylesheet" type="text/css" href="_static/custom.css" />
       <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
-      <link rel="stylesheet" type="text/css" href="_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css" />
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
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-  
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/clipboard.min.js"></script>
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-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -35,6 +28,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset" href="samples/dbscan.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -61,9 +55,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,205 +86,56 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#acceleration">Acceleration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#machine-learning-workflows">Machine Learning Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
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-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
 <li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
 </ul>
 </li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
@@ -324,17 +166,13 @@
              
   <section id="kaggle-kernels">
 <span id="kaggle"></span><h1>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Permalink to this heading"></a></h1>
-<p>See Kaggle notebooks created by Intel® Extension for Scikit-learn* developers.</p>
-<section id="acceleration">
-<h2>Acceleration<a class="headerlink" href="#acceleration" title="Permalink to this heading"></a></h2>
+<p>Check out Kaggle notebooks created by Intel® Extension for Scikit-learn* developers.</p>
+<p class="rubric">Acceleration</p>
 <p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook">Introduction to scikit-learn-intelex</a>
 provides a summary of the speedup you can achieve with Intel® Extension for Scikit-learn*.</p>
-</section>
-<section id="machine-learning-workflows">
-<h2>Machine Learning Workflows<a class="headerlink" href="#machine-learning-workflows" title="Permalink to this heading"></a></h2>
+<p class="rubric">Machine Learning Workflows</p>
 <p>Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.</p>
-<section id="kaggle-kernels-that-use-scikit-learn-and-intelex">
-<h3>Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-scikit-learn-and-intelex" title="Permalink to this heading"></a></h3>
+<p>Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
@@ -351,16 +189,12 @@ <h3>Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*
 </li>
 </ul>
 </div>
-</section>
-<section id="kaggle-kernels-that-use-automl-with-intelex">
-<h3>Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-automl-with-intelex" title="Permalink to this heading"></a></h3>
+<p>Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
 </ul>
 </div>
-</section>
-</section>
 </section>
 
 
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   <hr/>
 
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+<jinja2.runtime.BlockReference object at 0x7fca6704e390>
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     Cookies
@@ -420,7 +254,8 @@ <h3>Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:<a c
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
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diff --git a/2024.3/kaggle/automl.html b/2024.3/kaggle/automl.html
index dd5251cfbc..df51bd21b0 100644
--- a/2024.3/kaggle/automl.html
+++ b/2024.3/kaggle/automl.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -7,34 +9,26 @@
   <title>Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn* &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
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-    <link rel="next" title="Supported Algorithms" href="../algorithms.html" />
+    <link rel="next" title="Acceleration" href="../acceleration.html" />
     <link rel="prev" title="Kaggle Kernels for Regression Tasks" href="regression.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -61,9 +55,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,205 +86,56 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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-<li class="toctree-l4 current"><a class="current reference internal" href="#">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
-</ul>
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-</li>
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-<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
 </ul>
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+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
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+<ul class="current">
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+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Classification Tasks</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#binary-classification">Binary Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+<li class="toctree-l2"><a class="reference internal" href="regression.html">Regression Tasks</a></li>
+<li class="toctree-l2"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
 </ul>
 </li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
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-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
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+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -35,6 +28,7 @@
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -61,9 +55,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,205 +86,60 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
-<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul class="current">
-<li class="toctree-l4"><a class="reference internal" href="classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
-</ul>
-</li>
-<li class="toctree-l4 current"><a class="current reference internal" href="#">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="#stacking-regressors">Stacking Regressors</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="classification.html">Classification Tasks</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Regression Tasks</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#stacking-regressors">Stacking Regressors</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+<li class="toctree-l2"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
 </ul>
 </li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
-</ul>
-</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
 </ul>
-</li>
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+<p class="caption" role="heading"><span class="caption-text">License</span></p>
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index 4463d2c76c..118a8ad33b 100644
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+++ b/2024.3/license.html
@@ -1,3 +1,5 @@
+
+
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@@ -7,33 +9,25 @@
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@@ -60,9 +54,6 @@
           <a href="index.html" class="icon icon-home">
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           </a>
-              <div class="version">
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@@ -94,205 +85,51 @@
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+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
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 </ul>
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+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
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 <ul>
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+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul class="current">
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+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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@@ -113,6 +137,7 @@
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+
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diff --git a/2024.3/objects.inv b/2024.3/objects.inv
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 <ul>
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-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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-<p class="caption" role="heading"><span class="caption-text">More</span></p>
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             Intel(R) Extension for Scikit-learn*
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   </div>
   
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Quick Start</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
-</ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 </ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
 <li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
@@ -324,403 +160,90 @@
            <div itemprop="articleBody">
              
   <section id="quick-start">
-<h1>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this heading"></a></h1>
-<p>Get ready to elevate your scikit-learn code with Intel® Extension for Scikit-learn* and experience the benefits of accelerated performance in just a few simple steps.</p>
-<section id="compatibility-with-scikit-learn">
-<h2>Compatibility with Scikit-learn*<a class="headerlink" href="#compatibility-with-scikit-learn" title="Permalink to this heading"></a></h2>
-<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.</p>
-</section>
-<section id="integrate-intelex">
-<h2>Integrate Intel® Extension for Scikit-learn*<a class="headerlink" href="#integrate-intelex" title="Permalink to this heading"></a></h2>
-<section id="patching">
-<h3>Patching<a class="headerlink" href="#patching" title="Permalink to this heading"></a></h3>
-<p>Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension.
-This action is called <code class="docutils literal notranslate"><span class="pre">patching</span></code>. This is not a permanent change so you can always undo the patching if necessary.</p>
-<p>To patch Intel® Extension for Scikit-learn, use one of these methods:</p>
-<table class="docutils align-left">
-<thead>
-<tr class="row-odd"><th class="head"><p>Method</p></th>
-<th class="head"><p>Action</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Use a flag in the command line</p></td>
-<td><p>Run this command:</p>
+<span id="id1"></span><h1>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this heading"></a></h1>
+<p>You may enable patching in different ways:</p>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<p>These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.</p>
+</div>
+<ul>
+<li><p>Without editing the code of a scikit-learn application by using the following command line flag:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span> <span class="n">my_application</span><span class="o">.</span><span class="n">py</span>
 </pre></div>
 </div>
-</td>
-</tr>
-<tr class="row-odd"><td><p>Modify your script</p></td>
-<td><p>Add the following lines:</p>
+</li>
+<li><p>Directly from the script:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
 <span class="n">patch_sklearn</span><span class="p">()</span>
 </pre></div>
 </div>
-</td>
-</tr>
-<tr class="row-even"><td><p>Import an estimator from the <code class="docutils literal notranslate"><span class="pre">sklearnex</span></code> module</p></td>
-<td><p>Run this command:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
-</pre></div>
+</li>
+</ul>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<p>You have to import scikit-learn <strong>after</strong> these lines. Otherwise, the patching will not affect the original scikit-learn estimators.</p>
 </div>
-</td>
-</tr>
-</tbody>
-</table>
-<p>These patching methods are interchangeable.
-They support different enabling scenarios while producing the same result.</p>
-<p><strong>Example</strong></p>
-<p>This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.</p>
-<div class="literal-block-wrapper docutils container" id="id2">
-<div class="code-block-caption"><span class="caption-text">Example: Drop-In Patching</span><a class="headerlink" href="#id2" title="Permalink to this code"></a></div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>  <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-  <span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
-  <span class="n">patch_sklearn</span><span class="p">()</span>
-
-  <span class="c1"># You need to re-import scikit-learn algorithms after the patch</span>
-  <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
-
-  <span class="c1"># The use of the original Scikit-learn is not changed</span>
-  <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">0</span><span class="p">],</span>
-              <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-  <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-  <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;kmeans.labels_ = </span><span class="si">{</span><span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<ul>
+<li><p>Through importing the desired estimator from the sklearnex module in your script:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
 </pre></div>
 </div>
-</div>
-</section>
-<section id="global-patching">
-<h3>Global Patching<a class="headerlink" href="#global-patching" title="Permalink to this heading"></a></h3>
-<p>You can also use global patching to patch all your scikit-learn applications without any additional actions.</p>
-<p>Before you begin, make sure that you have read and write permissions for Scikit-learn files.</p>
-<p>With global patching, you can:</p>
-<table class="docutils align-left">
-<thead>
-<tr class="row-odd"><th class="head"><p>Task</p></th>
-<th class="head"><p>Action</p></th>
-<th class="head"><p>Note</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Patch all supported algorithms</p></td>
-<td><p>Run this command:</p>
+</li>
+<li><p>Through <a class="reference internal" href="what-is-patching.html#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
 </pre></div>
 </div>
-</td>
-<td><p>If you run the global patching command several times with different parameters, then only the last configuration is applied.</p></td>
-</tr>
-<tr class="row-odd"><td><p>Patch selected algorithms</p></td>
-<td><p>Use <code class="docutils literal notranslate"><span class="pre">--algorithm</span></code> or <code class="docutils literal notranslate"><span class="pre">-a</span></code> keys with a list of algorithms to patch. For example, to patch only <code class="docutils literal notranslate"><span class="pre">SVC</span></code> and <code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> estimators, run</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span>
-</pre></div>
-</div>
-</td>
-<td></td>
-</tr>
-<tr class="row-even"><td><p>Enable global patching via code</p></td>
-<td><p>Use the <code class="docutils literal notranslate"><span class="pre">patch_sklearn</span></code> function with the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
-<span class="n">patch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="kn">import</span> <span class="nn">sklearn</span>
-</pre></div>
-</div>
-</td>
-<td><p>After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.</p></td>
-</tr>
-<tr class="row-odd"><td><p>Disable patching notifications</p></td>
-<td><p>Use <code class="docutils literal notranslate"><span class="pre">--no-verbose</span></code> or <code class="docutils literal notranslate"><span class="pre">-nv</span></code> keys:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span> <span class="o">-</span><span class="n">nv</span>
-</pre></div>
-</div>
-</td>
-<td></td>
-</tr>
-<tr class="row-even"><td><p>Disable global patching</p></td>
-<td><p>Run this command:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">unpatch_sklearn</span>
-</pre></div>
-</div>
-</td>
-<td></td>
-</tr>
-<tr class="row-odd"><td><p>Disable global patching via code</p></td>
-<td><p>Use the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument in the <code class="docutils literal notranslate"><span class="pre">unpatch_sklearn</span></code> function</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
-<span class="n">unpatch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</li>
+</ul>
+<p class="rubric">Example</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="c1"># You need to re-import scikit-learn algorithms after the patch</span>
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">0</span><span class="p">],</span>
+              <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
+<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;kmeans.labels_ = </span><span class="si">{</span><span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-</td>
-<td></td>
-</tr>
-</tbody>
-</table>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>If you clone an environment with enabled global patching, it will already be applied in the new environment.</p>
-</div>
-</section>
-<section id="unpatching">
-<h3>Unpatching<a class="headerlink" href="#unpatching" title="Permalink to this heading"></a></h3>
-<p>To undo the patch (also called <cite>unpatching</cite>) is to return scikit-learn to original implementation and
-replace patched algorithms with the stock scikit-learn algorithms.</p>
-<p>To unpatch successfully, you must reimport the scikit-learn package:</p>
+<p>In the example above, you can see that the use of the original Scikit-learn
+has not changed. This behavior is achieved through drop-in patching.</p>
+<p>To <a class="reference internal" href="what-is-patching.html#term-unpatching"><span class="xref std std-term">undo the patch</span></a>, run:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sklearnex</span><span class="o">.</span><span class="n">unpatch_sklearn</span><span class="p">()</span>
-<span class="c1"># Re-import scikit-learn algorithms after the unpatch</span>
+<span class="c1"># Re-import scikit-learn algorithms after the unpatch:</span>
 <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
 </pre></div>
 </div>
-</section>
-</section>
-<section id="installation">
-<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h2>
-<nav class="contents local" id="contents">
-<ul class="simple">
-<li><p><a class="reference internal" href="#install-from-pypi" id="id6">Install from PyPI</a></p></li>
-<li><p><a class="reference internal" href="#install-from-anaconda-cloud" id="id7">Install from Anaconda* Cloud</a></p></li>
-<li><p><a class="reference internal" href="#build-from-sources" id="id8">Build from Sources</a></p></li>
-<li><p><a class="reference internal" href="#install-intel-r-ai-tools" id="id9">Install Intel*(R) AI Tools</a></p></li>
-</ul>
-</nav>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>To prevent version conflicts, we recommend creating and activating a new environment for Intel® Extension for Scikit-learn*.</p>
-</div>
-<section id="install-from-pypi">
-<h3><a class="toc-backref" href="#id6" role="doc-backlink">Install from PyPI</a><a class="headerlink" href="#install-from-pypi" title="Permalink to this heading"></a></h3>
-<p>Recommended by default.</p>
-<p>To install Intel® Extension for Scikit-learn*, run:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
-</pre></div>
-</div>
-<p><strong>Supported Configurations</strong></p>
-<table class="docutils align-left">
-<thead>
-<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
-<th class="head"><p>Python 3.8</p></th>
-<th class="head"><p>Python 3.9</p></th>
-<th class="head"><p>Python 3.10</p></th>
-<th class="head"><p>Python 3.11</p></th>
-<th class="head"><p>Python 3.12</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Linux* OS</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-</tr>
-<tr class="row-odd"><td><p>Windows* OS</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-</tr>
-</tbody>
-</table>
-</section>
-<section id="install-from-anaconda-cloud">
-<h3><a class="toc-backref" href="#id7" role="doc-backlink">Install from Anaconda* Cloud</a><a class="headerlink" href="#install-from-anaconda-cloud" title="Permalink to this heading"></a></h3>
-<p>To prevent version conflicts, we recommend installing <cite>scikit-learn-intelex</cite> into a new conda environment.</p>
-<div class="sphinx-tabs docutils container">
-<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-0-0-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-0-0-0" name="0-0" role="tab" tabindex="0">Conda-Forge channel</button><button aria-controls="panel-0-0-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-1" name="0-1" role="tab" tabindex="-1">Intel channel</button><button aria-controls="panel-0-0-2" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-2" name="0-2" role="tab" tabindex="-1">Main channel</button></div><div aria-labelledby="tab-0-0-0" class="sphinx-tabs-panel" id="panel-0-0-0" name="0-0" role="tabpanel" tabindex="0"><p>Recommended by default.</p>
-<p>To install, run:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span>
-</pre></div>
-</div>
-<table class="docutils align-left" id="id3">
-<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id3" title="Permalink to this table"></a></caption>
-<thead>
-<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
-<th class="head"><p>Python 3.8</p></th>
-<th class="head"><p>Python 3.9</p></th>
-<th class="head"><p>Python 3.10</p></th>
-<th class="head"><p>Python 3.11</p></th>
-<th class="head"><p>Python 3.12</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Linux* OS</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-</tr>
-<tr class="row-odd"><td><p>Windows* OS</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-</tr>
-</tbody>
-</table>
-</div><div aria-labelledby="tab-0-0-1" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-1" name="0-1" role="tabpanel" tabindex="0"><p>Recommended for the Intel® Distribution for Python users.</p>
-<p>To install, run:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">intel</span>
+<p>You may specify which algorithms to patch:</p>
+<ul>
+<li><p>Patching only one algorithm:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="c1"># The names match scikit-learn estimators</span>
+<span class="n">patch_sklearn</span><span class="p">(</span><span class="s2">&quot;SVC&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<table class="docutils align-left" id="id4">
-<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id4" title="Permalink to this table"></a></caption>
-<thead>
-<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
-<th class="head"><p>Python 3.8</p></th>
-<th class="head"><p>Python 3.9</p></th>
-<th class="head"><p>Python 3.10</p></th>
-<th class="head"><p>Python 3.11</p></th>
-<th class="head"><p>Python 3.12</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Linux* OS</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-</tr>
-<tr class="row-odd"><td><p>Windows* OS</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-<td><p>[CPU, GPU]</p></td>
-</tr>
-</tbody>
-</table>
-</div><div aria-labelledby="tab-0-0-2" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-2" name="0-2" role="tabpanel" tabindex="0"><p>To install, run:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
+</li>
+<li><p>Patching several algorithms:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="c1"># The names match scikit-learn estimators</span>
+<span class="n">patch_sklearn</span><span class="p">([</span><span class="s2">&quot;SVC&quot;</span><span class="p">,</span> <span class="s2">&quot;DBSCAN&quot;</span><span class="p">])</span>
 </pre></div>
 </div>
-<table class="docutils align-left" id="id5">
-<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id5" title="Permalink to this table"></a></caption>
-<thead>
-<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
-<th class="head"><p>Python 3.8</p></th>
-<th class="head"><p>Python 3.9</p></th>
-<th class="head"><p>Python 3.10</p></th>
-<th class="head"><p>Python 3.11</p></th>
-<th class="head"><p>Python 3.12</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Linux* OS</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-</tr>
-<tr class="row-odd"><td><p>Windows* OS</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-<td><p>[CPU]</p></td>
-</tr>
-</tbody>
-</table>
-</div></div>
-</section>
-<section id="build-from-sources">
-<h3><a class="toc-backref" href="#id8" role="doc-backlink">Build from Sources</a><a class="headerlink" href="#build-from-sources" title="Permalink to this heading"></a></h3>
-<p>See <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md">Installation instructions</a> to build Intel® Extension for Scikit-learn* from the sources.</p>
-</section>
-<section id="install-intel-r-ai-tools">
-<h3><a class="toc-backref" href="#id9" role="doc-backlink">Install Intel*(R) AI Tools</a><a class="headerlink" href="#install-intel-r-ai-tools" title="Permalink to this heading"></a></h3>
-<p>Download the Intel AI Tools <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html">here</a>. The extension is already included.</p>
-</section>
-</section>
-<section id="release-notes">
-<h2>Release Notes<a class="headerlink" href="#release-notes" title="Permalink to this heading"></a></h2>
-<p>See the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/releases">Release Notes</a> for each version of Intel® Extension for Scikit-learn*.</p>
-</section>
-<section id="system-requirements">
-<h2>System Requirements<a class="headerlink" href="#system-requirements" title="Permalink to this heading"></a></h2>
-<section id="hardware-requirements">
-<h3>Hardware Requirements<a class="headerlink" href="#hardware-requirements" title="Permalink to this heading"></a></h3>
-<div class="sphinx-tabs docutils container">
-<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-1-1-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-1-1-0" name="1-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-1-1-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-1-1-1" name="1-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-1-1-0" class="sphinx-tabs-panel" id="panel-1-1-0" name="1-0" role="tabpanel" tabindex="0"><p>All processors with <code class="docutils literal notranslate"><span class="pre">x86</span></code> architecture with at least one of the following instruction sets:</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>SSE2</p></li>
-<li><p>SSE4.2</p></li>
-<li><p>AVX2</p></li>
-<li><p>AVX512</p></li>
-</ul>
-</div></blockquote>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>ARM* architecture is not supported.</p>
-</div>
-</div><div aria-labelledby="tab-1-1-1" class="sphinx-tabs-panel" hidden="true" id="panel-1-1-1" name="1-1" role="tabpanel" tabindex="0"><ul class="simple">
-<li><p>All Intel® integrated and discrete GPUs</p></li>
-<li><p>Intel® GPU drivers</p></li>
-</ul>
-</div></div>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our <a class="reference internal" href="blogs.html#blogs"><span class="std std-ref">blogs</span></a>.</p>
-</div>
-</section>
-<section id="software-requirements">
-<h3>Software Requirements<a class="headerlink" href="#software-requirements" title="Permalink to this heading"></a></h3>
-<div class="sphinx-tabs docutils container">
-<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-2-2-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-2-2-0" name="2-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-2-2-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-2-2-1" name="2-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-2-2-0" class="sphinx-tabs-panel" id="panel-2-2-0" name="2-0" role="tabpanel" tabindex="0"><ul class="simple">
-<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
-<li><p>Windows* OS 10 or newer</p></li>
-<li><p>Windows* Server 2019 or newer</p></li>
-</ul>
-</div><div aria-labelledby="tab-2-2-1" class="sphinx-tabs-panel" hidden="true" id="panel-2-2-1" name="2-1" role="tabpanel" tabindex="0"><ul class="simple">
-<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
-<li><p>Windows* OS 10 or newer</p></li>
-<li><p>Windows* Server 2019 or newer</p></li>
-</ul>
-<div class="admonition important">
-<p class="admonition-title">Important</p>
-<p>If you use accelerators, refer to <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html">oneAPI DPC++/C++ Compiler System Requirements</a>.</p>
-</div>
-</div></div>
-<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:</p>
-<ul class="simple">
-<li><p>1.0.X</p></li>
-<li><p>1.1.X</p></li>
-<li><p>1.2.X</p></li>
-<li><p>1.3.X</p></li>
+</li>
 </ul>
-</section>
-<section id="memory-requirements">
-<h3>Memory Requirements<a class="headerlink" href="#memory-requirements" title="Permalink to this heading"></a></h3>
-<p>By default, algorithms in Intel® Extension for Scikit-learn* run in the multi-thread mode. This mode uses all available threads.
-Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.</p>
-<table class="docutils align-left">
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-<td><p>Both Scikit-learn and Intel® Extension for Scikit-learn* consume approximately the same amount of RAM.</p></td>
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-</tr>
-</tbody>
-</table>
-<p>In all Intel® Extension for Scikit-learn* algorithms with GPU support, computations run on device memory.
-The device memory must be large enough to store a copy of the entire dataset.
-You may also require additional device memory for internal arrays that are used in computation.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
-<p><a class="reference internal" href="samples.html#samples"><span class="std std-ref">Samples</span></a></p>
+<ul class="simple">
+<li><p><a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported algorithms and parameters</span></a></p></li>
+<li><p><a class="reference internal" href="samples.html#samples"><span class="std std-ref">Samples</span></a></p></li>
+<li><p><a class="reference internal" href="global-patching.html#global-patching"><span class="std std-ref">Global Patching</span></a></p></li>
+<li><p><a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/guide/intel-extension-for-scikit-learn-getting-started.html">Getting Started Guide</a></p></li>
+</ul>
 </div>
-</section>
-</section>
 </section>
 
 
@@ -728,13 +251,13 @@ <h3>Memory Requirements<a class="headerlink" href="#memory-requirements" title="
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-        <a href="samples.html" class="btn btn-neutral float-right" title="Samples" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="what-is-patching.html" class="btn btn-neutral float-right" title="Patching" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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diff --git a/2023.2/releases.html b/2024.3/releases.html
similarity index 73%
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rename to 2024.3/releases.html
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--- a/2023.2/releases.html
+++ b/2024.3/releases.html
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-  <![endif]-->
+  <title>Release Notes &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
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+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
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@@ -51,10 +55,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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@@ -63,6 +63,30 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.3"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
@@ -110,6 +134,7 @@
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
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+<jinja2.runtime.BlockReference object at 0x7fca6710dad0>
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   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.3"
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+      data-bs-toggle="dropdown"
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+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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   <script>
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diff --git a/2024.3/samples.html b/2024.3/samples.html
index 59dd7d47fc..bd7a396ca1 100644
--- a/2024.3/samples.html
+++ b/2024.3/samples.html
@@ -1,3 +1,5 @@
+
+
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+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
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+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="samples/knn_mnist.html" />
-    <link rel="prev" title="Quick Start" href="quick-start.html" />
+    <link rel="prev" title="Memory Requirements" href="memory-requirements.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
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@@ -61,9 +55,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,205 +86,66 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
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+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">ElasticNet for Airlines DepDelay dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -150,153 +127,34 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
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-<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
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-<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
-</ul>
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+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
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@@ -62,9 +56,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -96,205 +87,51 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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-</li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
 </ul>
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 </ul>
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 </ul>
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 <ul>
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+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
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 </ul>
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+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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@@ -38,6 +31,7 @@
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -64,9 +58,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
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   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
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@@ -98,99 +89,44 @@
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   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
 </ul>
-</li>
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+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
 </ul>
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+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">DBSCAN for spoken arabic digit dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -202,101 +138,23 @@
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
-</ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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@@ -38,6 +31,7 @@
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@@ -64,9 +58,6 @@
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           </a>
-              <div class="version">
-                2024.3
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@@ -98,92 +89,43 @@
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   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Kmeans for spoken arabic digit dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -191,112 +133,28 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
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+<p class="caption" role="heading"><span class="caption-text">License</span></p>
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@@ -98,32 +89,33 @@
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+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
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 <li class="toctree-l2 current"><a class="current reference internal" href="#">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
@@ -131,172 +123,37 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
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-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
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-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
-</ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
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-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
-</ul>
-</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
@@ -564,7 +421,7 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
   <hr/>
 
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       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
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@@ -1,3 +1,5 @@
+
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +31,7 @@
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -64,9 +58,6 @@
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             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,58 +89,38 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
 </ul>
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+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Lasso Regression for YearPredictionMSD dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
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@@ -157,146 +128,33 @@
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 </li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
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-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
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-<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
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 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
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-<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
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             Intel(R) Extension for Scikit-learn*
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-                2024.3
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@@ -98,65 +89,39 @@
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   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Linear Regression for YearPredictionMSD dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Normalize-the-data">Normalize the data</a></li>
@@ -164,139 +129,32 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
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-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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-</li>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
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-</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
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-<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
@@ -583,7 +441,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
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diff --git a/2024.3/samples/logistictic_regression_cifar.html b/2024.3/samples/logistictic_regression_cifar.html
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -64,9 +58,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,205 +89,71 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Logistic Regression for Cifar dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
-</ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
-</ul>
-</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
@@ -543,7 +400,7 @@ <h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss me
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@@ -1,3 +1,5 @@
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@@ -98,72 +89,40 @@
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -171,132 +130,31 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
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+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
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@@ -98,205 +89,71 @@
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+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
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-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
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-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Random Forest for Yolanda dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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 </ul>
 
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+  
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+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
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-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Rigde Regression for Airlines DepDelay dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -184,119 +132,29 @@
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 </ul>
 </li>
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-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
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-<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
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-</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
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@@ -98,205 +89,71 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul class="current">
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-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Support Vector Classification (SVC) for Adult dataset</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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 </li>
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-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
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-<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
-</ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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-</li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
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-</li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
-</ul>
-</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
@@ -539,7 +396,7 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
   <hr/>
 
   <div role="contentinfo">
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@@ -585,7 +442,8 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
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diff --git a/2024.3/samples/tsne.html b/2024.3/samples/tsne.html
index df1506332b..a286b962e3 100644
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@@ -1,3 +1,5 @@
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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@@ -62,9 +56,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -96,205 +87,51 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
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@@ -62,9 +56,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.3
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="#" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -96,205 +87,51 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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 </ul>
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-<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
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-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
 <li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
 </ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
-</ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
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index 7d4ad745fc..df114bfdd0 100644
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+++ b/2024.3/support.html
@@ -1,40 +1,34 @@
+
+
 <!DOCTYPE html>
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+  <title>Support &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
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@@ -61,9 +55,6 @@
           <a href="index.html" class="icon icon-home">
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@@ -95,205 +86,51 @@
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+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
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+<ul class="current">
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+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Support</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
 <ul>
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 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
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+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
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-  <section id="intel-r-extension-for-scikit-learn-support">
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-<p>We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn.</p>
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-<p>If you have a problem, check out the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">GitHub Issues</a> to see if the issue you want to address is already reported.</p>
-<p>You may find users that have encountered the same bug or have similar ideas for changes or updates.</p>
-<p>You can use issues to report a problem, make a feature request, or add comments on an existing issue.</p>
-</section>
+  <section id="support">
+<h1>Support<a class="headerlink" href="#support" title="Permalink to this heading"></a></h1>
+<p>Report issues, ask questions, and provide suggestions using:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">GitHub Issues</a></p></li>
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+<li><p><a class="reference external" href="https://community.intel.com/t5/Intel-Distribution-for-Python/bd-p/distribution-python">Forum</a></p></li>
+</ul>
+<p>You may reach out to project maintainers privately at <a class="reference external" href="mailto:onedal&#46;maintainers&#37;&#52;&#48;intel&#46;com">onedal<span>&#46;</span>maintainers<span>&#64;</span>intel<span>&#46;</span>com</a></p>
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diff --git a/2024.3/verbose.html b/2024.3/verbose.html
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@@ -61,9 +55,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
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@@ -95,205 +86,51 @@
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   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
-<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
-</ul>
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-<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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 </ul>
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-<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Features</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Verbose Mode</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
 </ul>
-</li>
+<p class="caption" role="heading"><span class="caption-text">License</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
@@ -332,12 +169,12 @@
 </div>
 <p>To enable verbose mode, set the <code class="docutils literal notranslate"><span class="pre">SKLEARNEX_VERBOSE</span></code> environment variable as shown below:</p>
 <ul>
-<li><p>On Linux* OS</p>
+<li><p>On Linux and MacOS:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
 </pre></div>
 </div>
 </li>
-<li><p>On Windows* OS</p>
+<li><p>On Windows:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
 </pre></div>
 </div>
@@ -366,13 +203,13 @@
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
         <a href="distributed-mode.html" class="btn btn-neutral float-left" title="Distributed Mode" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="deprecation.html" class="btn btn-neutral float-right" title="Deprecation Notice" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="releases.html" class="btn btn-neutral float-right" title="Release Notes" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f1246044790>
+<jinja2.runtime.BlockReference object at 0x7fca67c163d0>
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     Cookies
@@ -418,7 +255,8 @@
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
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diff --git a/latest/what-is-patching.html b/2024.3/what-is-patching.html
similarity index 85%
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--- a/latest/what-is-patching.html
+++ b/2024.3/what-is-patching.html
@@ -1,23 +1,26 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Patching &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="_static/custom.css" type="text/css" />
-      <link rel="stylesheet" href="_static/tabs.css" type="text/css" />
-    <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Patching &mdash; Intel(R) Extension for Scikit-learn* 2024.3 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="_static/tabs.css" />
+
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -25,6 +28,7 @@
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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@@ -51,10 +55,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,6 +63,30 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.3"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
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+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
@@ -110,6 +134,7 @@
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -258,7 +283,7 @@ <h2>How it works<a class="headerlink" href="#how-it-works" title="Permalink to t
   <hr/>
 
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@@ -283,6 +308,28 @@ <h2>How it works<a class="headerlink" href="#how-it-works" title="Permalink to t
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
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+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
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index a5c62ffc6d..d02d47c974 100644
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+      <script src="/scikit-learn-intelex/_static/jquery.js"></script>
+      <script src="/scikit-learn-intelex/_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="#" id="documentation_options" src="/scikit-learn-intelex/_static/documentation_options.js"></script>
+      <script src="/scikit-learn-intelex/_static/doctools.js"></script>
+      <script src="/scikit-learn-intelex/_static/sphinx_highlight.js"></script>
+      <script src="/scikit-learn-intelex/_static/clipboard.min.js"></script>
+      <script src="/scikit-learn-intelex/_static/copybutton.js"></script>
+      <script src="/scikit-learn-intelex/_static/version_switcher.js"></script>
+      <script src="/scikit-learn-intelex/_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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+  
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\ No newline at end of file
diff --git a/2024.6/_static/js/versions.js b/2024.6/_static/js/versions.js
new file mode 100644
index 0000000000..818bc9969f
--- /dev/null
+++ b/2024.6/_static/js/versions.js
@@ -0,0 +1,224 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = "True";
+const themeLanguageSelector = "True";
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${config.projects.translations
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/2024.6/_static/version_switcher.js b/2024.6/_static/version_switcher.js
index bc817160a6..162851d217 100644
--- a/2024.6/_static/version_switcher.js
+++ b/2024.6/_static/version_switcher.js
@@ -1,36 +1,31 @@
-fetch("https:///intel.github.io/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=>
-    load_versions(respText));
-
-
-function load_versions(json){
-    var button = document.getElementById('version-switcher-button')
-    var container = document.getElementById('version-switcher-dropdown')
-    var loc = window.location.href;
-    var s = document.createElement('select');
-    s.style = "border-radius:5px;"
-    const versions = JSON.parse(json);
-    for (entry of versions){
-        var o = document.createElement('option');
-        var optionText = '';
-        if ('name' in entry){
-            optionText = entry.name;
-        }else{
-            optionText = entry.version;
-        }
-        o.value = entry.url;
-        if (current_version == entry.version){
-            o.selected = true;
-            optionText = optionText;
-        }
-        o.innerHTML = optionText;
-        s.append(o);
-    }
-    s.addEventListener("change", (event)=> {
-        var current_url = new URL(window.location.href);
-        var path = current_url.pathname;
-        //strip version from path
-        var page_path = path.substring(project_name.length+current_version.length+3);
-        window.location.href = s.value + page_path;
-    });
-    container.append(s);
-}
\ No newline at end of file
+function load_versions(json){
+    var button = document.getElementById('version-switcher-button')
+    var container = document.getElementById('version-switcher-dropdown')
+    var loc = window.location.href;
+    var s = document.createElement('select');
+    s.style = "border-radius:5px;"
+    const versions = JSON.parse(json);
+    for (entry of versions){
+        var o = document.createElement('option');
+        var optionText = '';
+        if ('name' in entry){
+            optionText = entry.name;
+        }else{
+            optionText = entry.version;
+        }
+        o.value = entry.url;
+        if (current_version == entry.version){
+            o.selected = true;
+        }
+        o.innerHTML = optionText;
+        s.append(o);
+    }
+    s.addEventListener("change", ()=> {
+        var current_url = new URL(window.location.href);
+        var path = current_url.pathname;
+        //strip version from path
+        var page_path = path.substring(project_name.length+current_version.length+3);
+        window.location.href = s.value + page_path;
+    });
+    container.append(s);
+}
diff --git a/2024.6/algorithms.html b/2024.6/algorithms.html
index 780d0fb222..722544c7af 100644
--- a/2024.6/algorithms.html
+++ b/2024.6/algorithms.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -14,20 +16,16 @@
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/version_switcher.js"></script>
-        <script src="_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -35,6 +33,7 @@
     <link rel="prev" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" href="kaggle/automl.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -61,9 +60,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,6 +91,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
@@ -837,7 +834,7 @@ <h2>Scikit-learn tests<a class="headerlink" href="#scikit-learn-tests" title="Pe
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f93555998d0>
+<jinja2.runtime.BlockReference object at 0x7f44ae286f90>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -883,7 +880,8 @@ <h2>Scikit-learn tests<a class="headerlink" href="#scikit-learn-tests" title="Pe
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/blogs.html b/2024.6/blogs.html
index 2fdfcea839..d551e5bf09 100644
--- a/2024.6/blogs.html
+++ b/2024.6/blogs.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -14,20 +16,16 @@
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/version_switcher.js"></script>
-        <script src="_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -35,6 +33,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn* Tutorials &amp; Case Studies" href="tutorials.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -61,9 +60,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,6 +91,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
@@ -353,7 +350,7 @@
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f93555994e0>
+<jinja2.runtime.BlockReference object at 0x7f44ae38e990>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -399,7 +396,8 @@
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/contribute.html b/2024.6/contribute.html
index 62a26cb47f..7fc6e53273 100644
--- a/2024.6/contribute.html
+++ b/2024.6/contribute.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -14,20 +16,16 @@
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/version_switcher.js"></script>
-        <script src="_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -35,6 +33,7 @@
     <link rel="prev" title="Intel(R) Extension for Scikit-learn Support" href="support.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -61,9 +60,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,6 +91,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
@@ -372,7 +369,7 @@ <h2>Code Style<a class="headerlink" href="#code-style" title="Permalink to this
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f93559d9690>
+<jinja2.runtime.BlockReference object at 0x7f44aa646b10>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -418,7 +415,8 @@ <h2>Code Style<a class="headerlink" href="#code-style" title="Permalink to this
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/deprecation.html b/2024.6/deprecation.html
index bcb2d9b657..670b7bb1d1 100644
--- a/2024.6/deprecation.html
+++ b/2024.6/deprecation.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -14,20 +16,16 @@
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/version_switcher.js"></script>
-        <script src="_static/design-tabs.js"></script>
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+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -35,6 +33,7 @@
     <link rel="prev" title="Verbose Mode" href="verbose.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -61,9 +60,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,6 +91,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
@@ -344,7 +341,7 @@ <h2>macOS* Support<a class="headerlink" href="#macos-support" title="Permalink t
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f9355684d30>
+<jinja2.runtime.BlockReference object at 0x7f44adeda7d0>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -390,7 +387,8 @@ <h2>macOS* Support<a class="headerlink" href="#macos-support" title="Permalink t
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/distributed-mode.html b/2024.6/distributed-mode.html
index c58a93ab00..1ff2b38038 100644
--- a/2024.6/distributed-mode.html
+++ b/2024.6/distributed-mode.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -14,20 +16,16 @@
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/version_switcher.js"></script>
-        <script src="_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -35,6 +33,7 @@
     <link rel="prev" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" href="oneapi-gpu.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -61,9 +60,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,6 +91,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
@@ -346,7 +343,7 @@
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   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f9355aeb970>
+<jinja2.runtime.BlockReference object at 0x7f44ae182490>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -392,7 +389,8 @@
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/genindex.html b/2024.6/genindex.html
index 5c6c304ef8..27d6c074f3 100644
--- a/2024.6/genindex.html
+++ b/2024.6/genindex.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -13,26 +15,23 @@
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/version_switcher.js"></script>
-        <script src="_static/tabs.js"></script>
-        <script src="_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/tabs.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="#" />
     <link rel="search" title="Search" href="search.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -59,9 +58,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -93,6 +89,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
@@ -334,7 +331,7 @@ <h1 id="index">Index</h1>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f9355ae8580>
+<jinja2.runtime.BlockReference object at 0x7f44aa2f8150>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -380,7 +377,8 @@ <h1 id="index">Index</h1>
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/guide/acceleration.html b/2024.6/guide/acceleration.html
index 9ea9aa8a95..17cc002345 100644
--- a/2024.6/guide/acceleration.html
+++ b/2024.6/guide/acceleration.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -14,20 +16,16 @@
 
   
     <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -35,6 +33,7 @@
     <link rel="prev" title="Deprecation Notice" href="../deprecation.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -61,9 +60,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,6 +91,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -409,7 +406,7 @@ <h1>Tuning Guide<a class="headerlink" href="#tuning-guide" title="Permalink to t
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f9355ae5840>
+<jinja2.runtime.BlockReference object at 0x7f44ae1a1950>
 <a href=
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     Cookies
@@ -455,7 +452,8 @@ <h1>Tuning Guide<a class="headerlink" href="#tuning-guide" title="Permalink to t
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/index.html b/2024.6/index.html
index cd2c27b8f1..336f4a9aaa 100644
--- a/2024.6/index.html
+++ b/2024.6/index.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -14,26 +16,23 @@
 
   
     <link rel="shortcut icon" href="_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/version_switcher.js"></script>
-        <script src="_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="Quick Start" href="quick-start.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -60,9 +59,6 @@
           <a href="#" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -94,6 +90,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
@@ -395,7 +392,7 @@ <h3>Enable Intel(R) GPU optimizations<a class="headerlink" href="#enable-intel-r
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-  </div><script>
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index 8450f41e04..3b439c6a71 100644
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       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
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diff --git a/2024.6/kaggle/classification.html b/2024.6/kaggle/classification.html
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@@ -95,6 +91,7 @@
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     </div>
-  </div><script>
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+  <script>
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diff --git a/2024.6/kaggle/regression.html b/2024.6/kaggle/regression.html
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@@ -95,6 +91,7 @@
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-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
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index 10e882ebb8..f95446cc5b 100644
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@@ -61,9 +60,6 @@
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,6 +91,7 @@
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+  
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       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
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diff --git a/2024.6/quick-start.html b/2024.6/quick-start.html
index 13960a17f8..da31ba09c5 100644
--- a/2024.6/quick-start.html
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+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
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+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/tabs.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -62,9 +61,6 @@
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-                2024.6
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -96,6 +92,7 @@
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   </div>
   
+  
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       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
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diff --git a/2024.6/samples.html b/2024.6/samples.html
index 2f142509b9..953b75a9d8 100644
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+      <script src="_static/jquery.js"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
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@@ -61,9 +60,6 @@
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             Intel(R) Extension for Scikit-learn*
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -95,6 +91,7 @@
     </div>
   </div>
   
+  
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-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
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diff --git a/2024.6/samples/ElasticNet.html b/2024.6/samples/ElasticNet.html
index d121d695db..83184e6914 100644
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+
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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@@ -38,6 +36,7 @@
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -64,9 +63,6 @@
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@@ -98,6 +94,7 @@
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+  
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       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
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diff --git a/2024.6/samples/daal4py_data_science.html b/2024.6/samples/daal4py_data_science.html
index 89a485a356..c5ea3d9d06 100644
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+++ b/2024.6/samples/daal4py_data_science.html
@@ -1,3 +1,5 @@
+
+
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+      <script src="../_static/clipboard.min.js"></script>
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -96,6 +92,7 @@
     </div>
   </div>
   
+  
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     </div>
-  </div><script>
+  </div>
+  <script>
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diff --git a/2024.6/samples/dbscan.html b/2024.6/samples/dbscan.html
index fc23d8ed3d..c8656c38c4 100644
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+
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-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +36,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset" href="kmeans.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -64,9 +63,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -551,7 +548,7 @@ <h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f93560322f0>
+<jinja2.runtime.BlockReference object at 0x7f44ae183610>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -597,7 +594,8 @@ <h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/samples/kmeans.html b/2024.6/samples/kmeans.html
index c1325bfb42..ecfbeb9112 100644
--- a/2024.6/samples/kmeans.html
+++ b/2024.6/samples/kmeans.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -15,22 +17,18 @@
 
   
     <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +36,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" href="ridge_regression.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -64,9 +63,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -571,7 +568,7 @@ <h3>Compare inertia and number of iterations of patched Scikit-learn and origina
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f93554cda80>
+<jinja2.runtime.BlockReference object at 0x7f44ae64f4d0>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -617,7 +614,8 @@ <h3>Compare inertia and number of iterations of patched Scikit-learn and origina
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/samples/knn_mnist.html b/2024.6/samples/knn_mnist.html
index e5969f84b3..e7ecb78195 100644
--- a/2024.6/samples/knn_mnist.html
+++ b/2024.6/samples/knn_mnist.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -15,22 +17,18 @@
 
   
     <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +36,7 @@
     <link rel="prev" title="Samples" href="../samples.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -64,9 +63,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -564,7 +561,7 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f93555990f0>
+<jinja2.runtime.BlockReference object at 0x7f44ac819ad0>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -610,7 +607,8 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/samples/lasso_regression.html b/2024.6/samples/lasso_regression.html
index e967bc80a9..bb8cd8c5e8 100644
--- a/2024.6/samples/lasso_regression.html
+++ b/2024.6/samples/lasso_regression.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -15,22 +17,18 @@
 
   
     <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +36,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" href="ElasticNet.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -64,9 +63,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -588,7 +585,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f9355beb070>
+<jinja2.runtime.BlockReference object at 0x7f44ae4603d0>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -634,7 +631,8 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/samples/linear_regression.html b/2024.6/samples/linear_regression.html
index 1d36dafcf8..07e7d1f478 100644
--- a/2024.6/samples/linear_regression.html
+++ b/2024.6/samples/linear_regression.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -15,22 +17,18 @@
 
   
     <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +36,7 @@
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -64,9 +63,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -583,7 +580,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f9355be8b50>
+<jinja2.runtime.BlockReference object at 0x7f44ae0ef050>
 <a href=
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     Cookies
@@ -629,7 +626,8 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/samples/logistictic_regression_cifar.html b/2024.6/samples/logistictic_regression_cifar.html
index 910f18578b..d4f26e8b2f 100644
--- a/2024.6/samples/logistictic_regression_cifar.html
+++ b/2024.6/samples/logistictic_regression_cifar.html
@@ -1,3 +1,5 @@
+
+
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 <html class="writer-html5" lang="en">
 <head>
@@ -15,22 +17,18 @@
 
   
     <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +36,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="knn_mnist.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -64,9 +63,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -543,7 +540,7 @@ <h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss me
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f93555ef5e0>
+<jinja2.runtime.BlockReference object at 0x7f44aa656fd0>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -589,7 +586,8 @@ <h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss me
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/samples/nusvr_medical_charges.html b/2024.6/samples/nusvr_medical_charges.html
index 11e1aed48f..4bedd68991 100644
--- a/2024.6/samples/nusvr_medical_charges.html
+++ b/2024.6/samples/nusvr_medical_charges.html
@@ -1,3 +1,5 @@
+
+
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 <html class="writer-html5" lang="en">
 <head>
@@ -15,22 +17,18 @@
 
   
     <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +36,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" href="linear_regression.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -64,9 +63,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -552,7 +549,7 @@ <h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patche
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f9355be88e0>
+<jinja2.runtime.BlockReference object at 0x7f44ae29f290>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -598,7 +595,8 @@ <h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patche
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/samples/random_forest_yolanda.html b/2024.6/samples/random_forest_yolanda.html
index 2dc07413e0..9e02a70ea6 100644
--- a/2024.6/samples/random_forest_yolanda.html
+++ b/2024.6/samples/random_forest_yolanda.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -15,22 +17,18 @@
 
   
     <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +36,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset" href="nusvr_medical_charges.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -64,9 +63,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -534,7 +531,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f9355be8cd0>
+<jinja2.runtime.BlockReference object at 0x7f44ae1cbd10>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -580,7 +577,8 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
diff --git a/2024.6/samples/ridge_regression.html b/2024.6/samples/ridge_regression.html
index 084229c0aa..358b1beff8 100644
--- a/2024.6/samples/ridge_regression.html
+++ b/2024.6/samples/ridge_regression.html
@@ -1,3 +1,5 @@
+
+
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
@@ -15,22 +17,18 @@
 
   
     <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/version_switcher.js"></script>
-        <script src="../_static/design-tabs.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,6 +36,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" href="random_forest_yolanda.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -64,9 +63,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
@@ -586,7 +583,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f93554cd2a0>
+<jinja2.runtime.BlockReference object at 0x7f44aa3dde10>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -632,7 +629,8 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
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           SphinxRtdTheme.Navigation.enable(true);
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index c5e715d1f0..9b3ed04792 100644
--- a/2024.6/samples/svc_adult.html
+++ b/2024.6/samples/svc_adult.html
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -98,6 +94,7 @@
     </div>
   </div>
   
+  
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 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
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+<jinja2.runtime.BlockReference object at 0x7f44ae3da050>
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       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
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diff --git a/2024.6/samples/tsne.html b/2024.6/samples/tsne.html
index d85b7b49c3..01e1d896ff 100644
--- a/2024.6/samples/tsne.html
+++ b/2024.6/samples/tsne.html
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+
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     <link rel="shortcut icon" href="../_static/favicons.png"/>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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             Intel(R) Extension for Scikit-learn*
           </a>
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-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -96,6 +92,7 @@
     </div>
   </div>
   
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
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       role="listbox" aria-labelledby="">
       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
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diff --git a/2024.6/search.html b/2024.6/search.html
index 980205f8ed..d0ec9dfe80 100644
--- a/2024.6/search.html
+++ b/2024.6/search.html
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+      <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+      <script src="_static/doctools.js"></script>
+      <script src="_static/sphinx_highlight.js"></script>
+      <script src="_static/clipboard.min.js"></script>
+      <script src="_static/copybutton.js"></script>
+      <script src="_static/version_switcher.js"></script>
+      <script src="_static/tabs.js"></script>
+      <script src="_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                2024.6
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 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="#" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -96,6 +92,7 @@
     </div>
   </div>
   
+  
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       <!-- dropdown will be populated by javascript on page load -->
     </div>
-  </div><script>
+  </div>
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
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diff --git a/2024.6/searchindex.js b/2024.6/searchindex.js
index b4abd7876a..33bf4f63df 100644
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+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
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+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb b/2024.7/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/dbscan.ipynb b/2024.7/.doctrees/nbsphinx/samples/dbscan.ipynb
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/dbscan.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/kmeans.ipynb b/2024.7/.doctrees/nbsphinx/samples/kmeans.ipynb
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/kmeans.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/knn_mnist.ipynb b/2024.7/.doctrees/nbsphinx/samples/knn_mnist.ipynb
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/knn_mnist.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/lasso_regression.ipynb b/2024.7/.doctrees/nbsphinx/samples/lasso_regression.ipynb
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/lasso_regression.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/linear_regression.ipynb b/2024.7/.doctrees/nbsphinx/samples/linear_regression.ipynb
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/linear_regression.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb b/2024.7/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb b/2024.7/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb b/2024.7/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/ridge_regression.ipynb b/2024.7/.doctrees/nbsphinx/samples/ridge_regression.ipynb
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/ridge_regression.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/svc_adult.ipynb b/2024.7/.doctrees/nbsphinx/samples/svc_adult.ipynb
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/svc_adult.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/.doctrees/nbsphinx/samples/tsne.ipynb b/2024.7/.doctrees/nbsphinx/samples/tsne.ipynb
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2024.7/.doctrees/nbsphinx/samples/tsne.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+            Intel(R) Extension for Scikit-learn*
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#unpatching">Unpatching</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
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+<p>Unfortunately, we could not find the page you were looking for. Try:</p>
+<ul class="simple">
+<li><p>using <a class="reference internal" href="search.html"><span class="std std-ref">search</span></a> to browse documentation</p></li>
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+  <hr/>
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+      data-bs-toggle="dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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diff --git a/2024.7/_sources/404.rst.txt b/2024.7/_sources/404.rst.txt
new file mode 100644
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--- /dev/null
+++ b/2024.7/_sources/404.rst.txt
@@ -0,0 +1,28 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+:orphan:
+:nosearch:
+
+Page not found
+==============
+
+Unfortunately, we could not find the page you were looking for. Try:
+
+- using :ref:`search <search>` to browse documentation
+- checking the table of contents on the left
+- filing an `issue <https://github.com/intel/scikit-learn-intelex/issues>`_ or
+  starting a `discussion <https://github.com/intel/scikit-learn-intelex/discussions>`_ on GitHub
\ No newline at end of file
diff --git a/2024.7/_sources/algorithms.rst.txt b/2024.7/_sources/algorithms.rst.txt
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+++ b/2024.7/_sources/algorithms.rst.txt
@@ -0,0 +1,373 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _sklearn_algorithms:
+
+####################
+Supported Algorithms
+####################
+
+Applying |intelex| impacts the following scikit-learn algorithms:
+
+on CPU
+------
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVC`
+     - All parameters are supported
+     - No limitations
+   * - `NuSVC`
+     - All parameters are supported
+     - No limitations
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``cpp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - 
+       - For ``algorithm`` == `'kd_tree'`:
+       
+         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - For ``algorithm`` == `'brute'`:
+         
+         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Multi-output and sparse data are not supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` not in [`'lbfgs'`, `'newton-cg'`]
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVR`
+     - All parameters are supported
+     - No limitations
+   * - `NuSVR`
+     - All parameters are supported
+     - No limitations
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``cpp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Multi-output and sparse data are not supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported, `#observations` should be >= `#features`.
+   * - `Ridge`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``solver`` != `'auto'`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported, `#observations` should be >= `#features`.
+   * - `ElasticNet`
+     - All parameters are supported except:
+
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.
+   * - `Lasso`
+     - All parameters are supported except:
+
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+     - No limitations
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+     - Sparse data is not supported
+   * - `TSNE`
+     - All parameters are supported except:
+
+       - ``metric`` != 'euclidean' or `'minkowski'` with ``p`` != `2`
+
+       Refer to :ref:`TSNE acceleration details <acceleration_tsne>` to learn more.
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - 
+       - For ``algorithm`` == 'kd_tree':
+         
+         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - For ``algorithm`` == 'brute':
+         
+         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Sparse data is not supported
+
+Other tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `train_test_split`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `assert_all_finite`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `pairwise_distance`
+     - All parameters are supported except:
+     
+       - ``metric`` not in [`'cosine'`, `'correlation'`]
+     - Only dense data is supported
+   * - `roc_auc_score`
+     - All parameters are supported except:
+       
+       - ``average`` != `None`
+       - ``sample_weight`` != `None`
+       - ``max_fpr`` != `None`
+       - ``multi_class`` != `None`
+     - No limitations
+
+on GPU
+------
+
+.. seealso:: :ref:`oneapi_gpu`
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVC`
+     - All parameters are supported except:
+
+       - ``kernel`` = `'sigmoid_poly'`
+       - ``class_weight`` != `None`
+     - Only binary dense data is supported
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``cpp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` != `'newton-cg'`
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+       - ``penalty`` != `'l2'`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``cpp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Only dense data is supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported, `#observations` should be >= `#features`.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+       
+       ``Init`` = `'k-means++'` fallbacks to CPU.
+     - Sparse data is not supported
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+     
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+
+Scikit-learn tests
+------------------
+
+Monkey-patched scikit-learn classes and functions passes scikit-learn's own test
+suite, with few exceptions, specified in `deselected_tests.yaml
+<https://github.com/intel/scikit-learn-intelex/blob/main/deselected_tests.yaml>`__.
+
+The results of the entire latest scikit-learn test suite with |intelex|: `CircleCI
+<https://circleci.com/gh/intel/scikit-learn-intelex>`_.
\ No newline at end of file
diff --git a/2024.7/_sources/blogs.rst.txt b/2024.7/_sources/blogs.rst.txt
new file mode 100644
index 0000000000..2d9b0024e5
--- /dev/null
+++ b/2024.7/_sources/blogs.rst.txt
@@ -0,0 +1,35 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _blogs:
+
+Follow us on Medium
+--------------------
+We publish blogs on Medium, so `follow us <https://medium.com/intel-analytics-software/tagged/machine-learning>`_
+to learn tips and tricks for more efficient data analysis the help of |intelex|.
+Here are our latest blogs:
+
+- `Save Time and Money with Intel Extension for Scikit-learn <https://medium.com/intel-analytics-software/save-time-and-money-with-intel-extension-for-scikit-learn-33627425ae4>`_,
+- `Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors <https://medium.com/intel-analytics-software/superior-machine-learning-performance-on-the-latest-intel-xeon-scalable-processor-efdec279f5a3>`_,
+- `Leverage Intel Optimizations in Scikit-Learn <https://medium.com/intel-analytics-software/leverage-intel-optimizations-in-scikit-learn-f562cb9d5544>`_,
+- `Intel Gives Scikit-Learn the Performance Boost Data Scientists Need <https://medium.com/intel-analytics-software/intel-gives-scikit-learn-the-performance-boost-data-scientists-need-42eb47c80b18>`_,
+- `From Hours to Minutes: 600x Faster SVM <https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae>`_,
+- `Improve the Performance of XGBoost and LightGBM Inference <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_,
+- `Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit <https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a>`_,
+- `Accelerate Your scikit-learn Applications <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_,
+- `Accelerate Linear Models for Machine Learning <https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe>`_,
+- `Accelerate K-Means Clustering <https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1>`_.
+- `Why Pay More for Machine Learning? <https://medium.com/intel-analytics-software/why-pay-more-for-machine-learning-893683bd78e4>`_.
diff --git a/2024.7/_sources/contribute.rst.txt b/2024.7/_sources/contribute.rst.txt
new file mode 100644
index 0000000000..28283bd9d8
--- /dev/null
+++ b/2024.7/_sources/contribute.rst.txt
@@ -0,0 +1,64 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+##################
+How to Contribute
+##################
+
+As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn. 
+This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.
+
+Licensing
+----------------------------------
+Intel(R) Extension for Scikit-learn uses the `Apache 2.0 License <https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE>`_. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.
+
+Pull Requests
+----------------------------------
+No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.
+
+Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:
+
+::
+
+  git config --global user.name "Kate Developer"
+  git config --global user.email kate.developer@company.com
+
+Before Contributing Changes
+********************************************
+
+Make sure you can build the product and run all the tests with your patch.
+For a larger feature, provide a relevant test.
+Document your code. Our project uses reStructuredText for documentation.
+For new file(s), specify the appropriate copyright year in the first line.
+Submit a pull request into the master branch.
+Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.
+
+Code Style
+-------------------
+
+We use `black <https://black.readthedocs.io/en/stable/>`_ and `isort <https://pycqa.github.io/isort/>`_ formatters for Python* code. 
+The line length is 90 characters; use default options otherwise. You can find the linter configuration in `.pyproject.toml <https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml>`_.
+
+A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.
+
+Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:
+
+::
+
+  pip install pre-commit
+  pre-commit install
+
+
diff --git a/2023.2/_sources/deprecation.rst.txt b/2024.7/_sources/deprecation.rst.txt
similarity index 100%
rename from 2023.2/_sources/deprecation.rst.txt
rename to 2024.7/_sources/deprecation.rst.txt
diff --git a/2024.7/_sources/distributed-mode.rst.txt b/2024.7/_sources/distributed-mode.rst.txt
new file mode 100644
index 0000000000..3b152b68a0
--- /dev/null
+++ b/2024.7/_sources/distributed-mode.rst.txt
@@ -0,0 +1,30 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _distributed:
+
+Distributed Mode
+================
+
+.. note::
+
+   |intelex| contains scikit-learn patching functionality that was originally available in 
+   `daal4py <https://github.com/intel/scikit-learn-intelex/tree/main/daal4py>`_ package.
+   We recommend you to use scikit-learn-intelex package instead of daal4py.
+   You can learn more about daal4py in `daal4py documentation <https://intelpython.github.io/daal4py>`_.
+
+While daal4py is available in `distribued mode <https://intelpython.github.io/daal4py/scaling.html>`_,
+|intelex| does not currently offer this functionality.
diff --git a/2024.7/_sources/guide/acceleration.rst.txt b/2024.7/_sources/guide/acceleration.rst.txt
new file mode 100644
index 0000000000..952ee73539
--- /dev/null
+++ b/2024.7/_sources/guide/acceleration.rst.txt
@@ -0,0 +1,86 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+########################
+Tuning Guide
+########################
+
+The performance of some algorithms changes based on the parameters that are used.
+This section denotes the details of such cases.
+
+Refer to :ref:`sklearn_algorithms` to see the full list of algorithms, parameters, and data formats supported in |intelex|.
+
+.. _acceleration_tsne:
+
+TSNE
+----
+
+TSNE algorithm consists of two components: KNN and Gradient Descent.
+The overall acceleration of TSNE depends on the acceleration of each of these algorithms.
+
+- The KNN part of the algorithm supports all parameters except:
+
+  - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+- The Gradient Descent part of the algorithm supports all parameters except:
+
+  - ``n_components`` = `3`
+  - ``method`` = `'exact'`
+  - ``verbose`` != `0`
+
+To get better performance, use parameters supported by both components.
+
+.. _acceleration_rf:
+
+Random Forest
+-------------
+
+Random Forest models accelerated with |intelex| and using the `hist` splitting
+method discretize training data by creating a histogram with a configurable
+number of bins. The following keyword arguments can be used to influence the
+created histogram.
+
+.. list-table::
+   :widths: 10 10 10 30
+   :header-rows: 1
+   :align: left
+
+   * - Keyword argument
+     - Possible values
+     - Default value
+     - Description
+   * - ``maxBins``
+     - `[0, inf)`
+     - ``256``
+     - Number of bins in the histogram with the discretized training data. The
+       value ``0`` disables data discretization.
+   * - ``minBinSize``
+     - `[1, inf)`
+     - ``5``
+     - Minimum number of training data points in each bin after discretization.
+   * - ``binningStrategy``
+     - ``quantiles, averages``
+     - ``quantiles``
+     - Selects the algorithm used to calculate bin edges. ``quantiles``
+       results in bins with a similar amount of training data points. ``averages``
+       divides the range of values observed in the training data set into
+       equal-width bins of size `(max - min) / maxBins`.
+
+Note that using discretized training data can greatly accelerate model training
+times, especially for larger data sets. However, due to the reduced fidelity of
+the data, the resulting model can present worse performance metrics compared to
+a model trained on the original data. In such cases, the number of bins can be
+increased with the ``maxBins`` parameter, or binning can be disabled entirely by
+setting ``maxBins=0``.
diff --git a/2024.7/_sources/index.rst.txt b/2024.7/_sources/index.rst.txt
new file mode 100644
index 0000000000..62055385c8
--- /dev/null
+++ b/2024.7/_sources/index.rst.txt
@@ -0,0 +1,134 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |intelex_repo| replace:: |intelex| repository
+.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
+
+.. _index:
+
+#########
+|intelex|
+#########
+
+Intel(R) Extension for Scikit-learn is a **free software AI accelerator** designed to deliver up to **100X** faster performance for your existing scikit-learn code.
+The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.
+
+.. rubric:: Designed for Data Scientists and Framework Designers
+
+
+Use Intel(R) Extension for Scikit-learn, to:
+
+* Speed up training and inference by up to 100x with the equivalent mathematical accuracy
+* Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs
+* Integrate the extension into your existing Scikit-learn applications without code modifications
+* Enable and disable the extension with a couple of lines of code or at the command line
+
+Intel(R) Extension for Scikit-learn is also a part of `Intel(R) AI Tools <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html>`_.
+
+
+.. image:: _static/scikit-learn-acceleration.PNG
+  :width: 800
+
+
+These performance charts use benchmarks that you can find in the `scikit-learn bench repository <https://github.com/IntelPython/scikit-learn_bench>`_. 
+
+
+Supported Algorithms 
+---------------------
+
+See all of the :ref:`sklearn_algorithms`. 
+
+
+Intel(R) Optimizations
+----------------------------------
+
+Enable Intel(R) CPU Optimizations
+*********************************
+
+::
+
+   import numpy as np
+   from sklearnex import patch_sklearn
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+Enable Intel(R) GPU optimizations
+*********************************
+::
+
+   import numpy as np
+   import dpctl
+   from sklearnex import patch_sklearn, config_context
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   with config_context(target_offload="gpu:0"):
+       clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+
+
+.. toctree::
+   :caption: Get Started
+   :hidden:
+   :maxdepth: 3
+
+   quick-start.rst
+   samples.rst
+   kaggle.rst
+   
+.. toctree::
+   :caption: Developer Guide
+   :hidden:
+   :maxdepth: 2
+
+   algorithms.rst
+   oneAPI and GPU support <oneapi-gpu.rst>
+   distributed-mode.rst
+   verbose.rst
+   deprecation.rst
+   
+
+.. toctree::
+   :caption: Performance
+   :hidden:
+   :maxdepth: 2
+
+   guide/acceleration.rst
+
+.. toctree::
+   :caption: Learn 
+   :hidden:
+   :maxdepth: 2
+
+   Tutorials & Case Studies <tutorials.rst>
+   Medium Blogs <blogs.rst>
+
+.. toctree::
+   :caption: More
+   :hidden:
+   :maxdepth: 2
+   
+   Support <support.rst>
+   contribute.rst
+   license.rst
diff --git a/2024.7/_sources/kaggle.rst.txt b/2024.7/_sources/kaggle.rst.txt
new file mode 100644
index 0000000000..364ab723d1
--- /dev/null
+++ b/2024.7/_sources/kaggle.rst.txt
@@ -0,0 +1,49 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _kaggle:
+
+####################
+Kaggle Kernels
+####################
+
+See Kaggle notebooks created by |intelex| developers.
+
+Acceleration
+---------------------------------
+
+`Introduction to scikit-learn-intelex <https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook>`_
+provides a summary of the speedup you can achieve with |intelex|.
+
+Machine Learning Workflows
+---------------------------------
+
+Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.
+
+Kaggle kernels that use scikit-learn and |intelex|:
+******************************************************************
+
+.. toctree::
+
+   Classification Tasks <kaggle/classification.rst>
+   Regression Tasks <kaggle/regression.rst>
+
+Kaggle kernels that use AutoML with |intelex|:
+******************************************************************
+
+.. toctree::
+
+   AutoML Workflows <kaggle/automl.rst>
diff --git a/2024.7/_sources/kaggle/automl.rst.txt b/2024.7/_sources/kaggle/automl.rst.txt
new file mode 100644
index 0000000000..0156e5ec89
--- /dev/null
+++ b/2024.7/_sources/kaggle/automl.rst.txt
@@ -0,0 +1,66 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |automl_with_intelex_jun| replace:: AutoML MultiClass Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with |intelex| 
+.. _automl_with_intelex_jun: https://www.kaggle.com/alex97andreev/tps-jun-autogluon-with-sklearnex
+
+.. |automl_with_intelex_tps_oct| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest) using AutoGluon with |intelex|
+.. _automl_with_intelex_tps_oct: https://www.kaggle.com/lordozvlad/fast-automl-with-intel-extension-for-scikit-learn/notebook
+
+.. |automl_with_intelex_tps_nov| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using EvalML and AutoGluon with |intelex|
+.. _automl_with_intelex_tps_nov: https://www.kaggle.com/lordozvlad/tps-nov-automl-with-intel-extension
+
+.. |automl_with_intelex_titanic| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with |intelex|
+.. _automl_with_intelex_titanic: https://www.kaggle.com/lordozvlad/titanic-automl-with-intel-extension-for-sklearn/notebook
+
+.. |automl_with_intelex_tps_jan| replace:: AutoML Binary Classification (Random Forest, SVR, Blending) using PyCaret with |intelex|
+.. _automl_with_intelex_tps_jan: https://www.kaggle.com/code/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex/notebook
+
+
+Kaggle Kernels that use AutoML and |intelex|
+--------------------------------------------
+
+The following Kaggle kernels show how to patch autoML frameworks with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 40 20
+
+   * - Kernel
+     - Goal
+   * - |automl_with_intelex_jun|_
+
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+   * - |automl_with_intelex_titanic|_
+
+       **Data:** Titanic datset
+     - Predict whether a passenger survivies
+   * - |automl_with_intelex_tps_oct|_
+
+       **Data:** [TPS Oct 2021] Synthetic molecular response data
+     - Predict the biological response of molecules given various chemical properties
+   * - |automl_with_intelex_tps_nov|_
+
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+     - Identify spam emails via features extracted from the email
+   * - |automl_with_intelex_tps_jan|_
+
+       **Data:** [TPS Jan 2022] Fictional Sales data
+     - Predict the corresponding item sales for each date-country-store-item combination
\ No newline at end of file
diff --git a/2024.7/_sources/kaggle/classification.rst.txt b/2024.7/_sources/kaggle/classification.rst.txt
new file mode 100644
index 0000000000..5d7b94345f
--- /dev/null
+++ b/2024.7/_sources/kaggle/classification.rst.txt
@@ -0,0 +1,244 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Kaggle Kernels for Classification Tasks
+***************************************
+
+The following Kaggle kernels show how to patch scikit-learn with |intelex| for various classification tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+Binary Classification
++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Logistic Regression for Binary Classification
+       <https://www.kaggle.com/alexeykolobyanin/tps-nov-log-regression-with-sklearnex-17x-speedup>`_
+     
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+
+     - Identify spam emails via features extracted from the email
+     - 
+
+       - data preprocessing (normalization)
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Feature Importance in Random Forest for Binary Classification
+       <https://www.kaggle.com/code/lordozvlad/fast-feature-importance-using-scikit-learn-intelex/notebook>`_
+     
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+
+     - Identify spam emails via features extracted from the email
+     - 
+      
+       - reducing DataFrame memory usage
+       - computing feature importance with ELI5 and the default scikit-learn permutation importance
+       - training using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest for Binary Classification
+       <https://www.kaggle.com/andreyrus/tps-apr-rf-with-intel-extension-for-scikit-learn>`_
+     
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+      
+       - data preprocessing
+       - feature construction
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for Binary Classification
+       <https://www.kaggle.com/napetrov/tps04-svm-with-intel-extension-for-scikit-learn>`_
+       
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+     
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) with Feature Preprocessing for Binary Classification
+       <https://www.kaggle.com/napetrov/tps04-svm-with-scikit-learn-intelex>`_
+      
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+     
+       - data preprocessing
+       - feature engineering
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+MultiClass Classification
++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Logistic Regression for MultiClass Classification with Quantile Transformer
+       <https://www.kaggle.com/kppetrov/tps-jun-fast-logreg-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+
+       - data preprocessing with Quantile Transformer
+       - training and prediction using scikit-learn-intelex
+       - search for optimal paramters using Optuna
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for MultiClass Classification
+       <https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS May 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+
+   * - `Stacking Classifer with Logistic Regression, kNN, Random Forest, and Quantile Transformer
+       <https://www.kaggle.com/owerbat/tps-jun-fast-stacking-with-scikit-learn-intelex>`_
+      
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+
+       - data preprocessing: one-hot encoding, dimensionality reduction with PCA, normalization
+       - creating a stacking classifier with logistic regression, kNN, and random forest,
+         and a pipeline of Quantile Transformer and another logistic regression as a final estimator
+       - searching for optimal parameters for the stacking classifier
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for MultiClass Classification
+       <https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup>`_
+       
+       **Data:** [TPS Dec 2021] Synthetic Forest Cover Type data
+     - Predict the forest cover type
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Feature Importance in Random Forest for MultiClass Classification
+       <https://www.kaggle.com/code/lordozvlad/tps-dec-fast-feature-importance-with-sklearnex>`_
+     
+       **Data:** [TPS Dec 2021] Synthetic Forest Cover Type data
+
+     - Predict the forest cover type
+     - 
+      
+       - reducing DataFrame memory usage
+       - computing feature importance with ELI5
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `k-Nearest Neighbors (kNN) for MultiClass Classification
+       <https://www.kaggle.com/code/alexeykolobyanin/tps-feb-knn-with-sklearnex-13x-speedup>`_
+       
+       **Data:** [TPS Feb 2022] Bacteria DNA
+     - Predict bacteria species based on repeated lossy measurements of DNA snippets
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Classification Tasks in Computer Vision
++++++++++++++++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Support Vector Classification (SVC) for MultiClass Classification (CV task)
+       <https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-mnist?scriptVersionId=58739300>`_
+       
+       **Data:** Digit Recognizer (MNIST)
+     - Recognize hand-written digits
+     - 
+       
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `k-Nearest Neighbors (kNN) for MultiClass Classification (CV task)
+       <https://www.kaggle.com/kppetrov/fast-knn-using-scikit-learn-intelex-for-mnist?scriptVersionId=58738635>`_
+       
+       **Data:** Digit Recognizer (MNIST)
+     - Recognize hand-written digits
+     - 
+     
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Classification Tasks in Natural Language Processing
++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Support Vector Classification (SVC) for a Binary Classification (NLP task)
+       <https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-nlp?scriptVersionId=58739339>`_
+       
+       **Data:** Natural Language Processing with Disaster Tweets
+     - Predict which tweets are about real disasters and which ones are not
+     - 
+     
+       - data preprocessing
+       - TF-IDF calculation
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `One-vs-Rest Support Vector Machine (SVM) with Text Data for MultiClass Classification
+       <https://www.kaggle.com/kppetrov/using-scikit-learn-intelex-for-what-s-cooking?scriptVersionId=58739642>`_
+      
+       **Data:** What's Cooking
+     - Use recipe ingredients to predict the cuisine
+     - 
+     
+       - feature extraction using TfidfVectorizer
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for Binary Classification with Sparse Data (NLP task)
+       <https://www.kaggle.com/code/alex97andreev/fast-svm-for-sparse-data-from-nlp-problem>`_
+       
+       **Data:** Stack Overflow questions
+     - Predict the binary quality rating for Stack Overflow questions
+     - 
+     
+       - data preprocessing
+       - TF-IDF calculation
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
diff --git a/2024.7/_sources/kaggle/regression.rst.txt b/2024.7/_sources/kaggle/regression.rst.txt
new file mode 100644
index 0000000000..abdbebc15b
--- /dev/null
+++ b/2024.7/_sources/kaggle/regression.rst.txt
@@ -0,0 +1,156 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Kaggle Kernels for Regression Tasks
+************************************
+
+The following Kaggle kernels show how to patch scikit-learn with |intelex| for various regression tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+Using a Single Regressor
+++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel
+       <https://www.kaggle.com/kppetrov/tps-jul-2021-baseline-with-nusvr-model/notebook>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - data preprocessing
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+   * - `Nu Support Vector Regression (nuSVR)
+       <https://www.kaggle.com/alexeykolobyanin/tps-aug-nusvr-with-intel-extension-for-sklearn>`__
+     
+       **Data:** [TPS Aug 2021] Synthetic loan data
+     - Calculate loss associated with a loan defaults
+     -
+
+       - data preprocessing
+       - feature engineering
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Nu Support Vector Regression (nuSVR)
+       <https://www.kaggle.com/alexeykolobyanin/house-prices-nusvr-sklearn-intelex-4x-speedup>`__
+       
+       **Data:** House Prices dataset
+     - Predict sale prices for a property based on its characteristics
+     -
+
+       - data preprocessing
+       - exploring outliers
+       - feature engineering
+       - filling missing values
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest Regression
+       <https://www.kaggle.com/pahandrovich/tps-jul-2021-fast-randomforest-with-sklearnex>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - checking correlation between features
+       - search for best paramters using GridSearchCV
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Random Forest Regression with Feature Engineering
+       <https://www.kaggle.com/alexeykolobyanin/tps-jul-rf-with-intel-extension-for-scikit-learn>`_
+     
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - data preprocessing
+       - feature engineering
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest Regression with Feature Importance Computation
+       <https://www.kaggle.com/code/lordozvlad/tps-mar-fast-workflow-using-scikit-learn-intelex>`_
+
+       **Data:** [TPS Mar 2022] Spatio-temporal traffic data
+     - Forecast twelve-hours of traffic flow in a major U.S. metropolitan area
+     -
+
+       - feature engineering
+       - computing feature importance with ELI5
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Ridge Regression
+       <https://www.kaggle.com/alexeykolobyanin/tps-sep-ridge-with-sklearn-intelex-2x-speedup>`_
+     
+     
+       **Data:** [TPS Sep 2021] Synthetic insurance data
+     - Predict the probability of a customer making a claim upon an insurance policy
+     -
+
+       - data preprocessing
+       - filling missing values
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Stacking Regressors
++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Stacking Regressor with Random Fores, SVR, and LASSO
+       <https://www.kaggle.com/alexeykolobyanin/tps-jul-stacking-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - feature engineering
+       - creating a stacking regressor
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data
+       <https://www.kaggle.com/alexeykolobyanin/predict-sales-stacking-with-scikit-learn-intelex>`_
+       
+       **Data:** Predict Future Sales dataset
+     - Predict total sales for every product and store in the next month based on daily sales data
+     -
+
+       - data preprocessing
+       - creating a stacking regressor
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
diff --git a/2023.2/_sources/license.rst.txt b/2024.7/_sources/license.rst.txt
similarity index 92%
rename from 2023.2/_sources/license.rst.txt
rename to 2024.7/_sources/license.rst.txt
index 945542761b..df68abaea6 100644
--- a/2023.2/_sources/license.rst.txt
+++ b/2024.7/_sources/license.rst.txt
@@ -20,4 +20,4 @@
 License
 ############################################
 
-License: https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE
\ No newline at end of file
+License: https://github.com/intel/scikit-learn-intelex/blob/main/LICENSE
\ No newline at end of file
diff --git a/2024.7/_sources/oneapi-gpu.rst.txt b/2024.7/_sources/oneapi-gpu.rst.txt
new file mode 100644
index 0000000000..37bfbf3a92
--- /dev/null
+++ b/2024.7/_sources/oneapi-gpu.rst.txt
@@ -0,0 +1,114 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _oneapi_gpu:
+
+##############################################################
+oneAPI and GPU support in |intelex|
+##############################################################
+
+|intelex| supports oneAPI concepts, which
+means that algorithms can be executed on different devices: CPUs and GPUs.
+This is done via integration with
+`dpctl <https://intelpython.github.io/dpctl/latest/index.html>`_ package that
+implements core oneAPI concepts like queues and devices.
+
+Prerequisites
+-------------
+
+For execution on GPU, DPC++ compiler runtime and driver are required. Refer to `DPC++ system
+requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_ for details.
+
+DPC++ compiler runtime can be installed either from PyPI or Anaconda:
+
+- Install from PyPI::
+
+     pip install dpcpp-cpp-rt
+
+- Install using Conda via the Intel repository::
+
+     conda install dpcpp_cpp_rt -c https://software.repos.intel.com/python/conda/
+
+Device offloading
+-----------------
+
+|intelex| offers two options for running an algorithm on a
+specific device with the help of dpctl:
+
+- Pass input data as `dpctl.tensor.usm_ndarray <https://intelpython.github.io/dpctl/latest/docfiles/dpctl/usm_ndarray.html#dpctl.tensor.usm_ndarray>`_ to the algorithm.
+
+  The computation will run on the device where the input data is
+  located, and the result will be returned as :code:`usm_ndarray` to the same
+  device.
+
+  .. note::
+    All the input data for an algorithm must reside on the same device.
+
+  .. warning::
+    The :code:`usm_ndarray` can only be consumed by the base methods
+    like :code:`fit`, :code:`predict`, and :code:`transform`.
+    Note that only the algorithms in |intelex| support
+    :code:`usm_ndarray`. The algorithms from the stock version of scikit-learn
+    do not support this feature.
+- Use global configurations of |intelex|\*:
+  
+  1. The :code:`target_offload` option can be used to set the device primarily
+     used to perform computations. Accepted data types are :code:`str` and
+     :code:`dpctl.SyclQueue`. If you pass a string to :code:`target_offload`,
+     it should either be ``"auto"``, which means that the execution
+     context is deduced from the location of input data, or a string
+     with SYCL* filter selector. The default value is ``"auto"``.
+  
+  2. The :code:`allow_fallback_to_host` option
+     is a Boolean flag. If set to :code:`True`, the computation is allowed 
+     to fallback to the host device when a particular estimator does not support
+     the selected device. The default value is :code:`False`.
+
+These options can be set using :code:`sklearnex.set_config()` function or
+:code:`sklearnex.config_context`. To obtain the current values of these options,
+call :code:`sklearnex.get_config()`.
+
+.. note::
+     Functions :code:`set_config`, :code:`get_config` and :code:`config_context`
+     are always patched after the :code:`sklearnex.patch_sklearn()` call.
+
+.. rubric:: Compatibility considerations
+
+For compatibility reasons, algorithms in |intelex| may be offloaded to the device using
+:code:`daal4py.oneapi.sycl_context`. However, it is recommended to use one of the options
+described above for device offloading instead of using :code:`sycl_context`.
+
+Example
+-------
+
+An example on how to patch your code with Intel CPU/GPU optimizations:
+
+.. code-block:: python
+
+   from sklearnex import patch_sklearn, config_context
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   with config_context(target_offload="gpu:0"):
+      clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+
+.. note:: Current offloading behavior restricts fitting and inference of any models to be
+     in the same context or absence of context. For example, a model trained in the GPU context with
+     target_offload="gpu:0" throws an error if the inference is made outside the same GPU context.
diff --git a/2024.7/_sources/quick-start.rst.txt b/2024.7/_sources/quick-start.rst.txt
new file mode 100644
index 0000000000..bc6ac8798a
--- /dev/null
+++ b/2024.7/_sources/quick-start.rst.txt
@@ -0,0 +1,405 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |intelex_repo| replace:: |intelex| repository
+.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
+
+####################
+Quick Start
+####################
+
+Get ready to elevate your scikit-learn code with |intelex| and experience the benefits of accelerated performance in just a few simple steps. 
+
+Compatibility with Scikit-learn*
+---------------------------------
+
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.
+
+Integrate |intelex|
+--------------------
+
+Patching 
+**********************
+
+Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension. 
+This action is called ``patching``. This is not a permanent change so you can always undo the patching if necessary.
+
+To patch Intel® Extension for Scikit-learn, use one of these methods: 
+
+.. list-table:: 
+   :header-rows: 1
+   :align: left
+
+   * - Method
+     - Action
+   * - Use a flag in the command line
+     - Run this command:
+
+       :: 
+         
+          python -m sklearnex my_application.py
+   * - Modify your script 
+     - Add the following lines:
+
+       ::
+ 
+          from sklearnex import patch_sklearn
+          patch_sklearn()   
+   * - Import an estimator from the ``sklearnex`` module
+     - Run this command:
+
+       ::
+
+          from sklearnex.neighbors import NearestNeighbors
+
+
+
+These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.
+
+   
+**Example**
+
+This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.
+  
+.. code-block:: python
+  :caption: Example: Drop-In Patching
+   
+    import numpy as np
+    from sklearnex import patch_sklearn
+    patch_sklearn()
+
+    # You need to re-import scikit-learn algorithms after the patch
+    from sklearn.cluster import KMeans
+  
+    # The use of the original Scikit-learn is not changed
+    X = np.array([[1,  2], [1,  4], [1,  0],
+                [10, 2], [10, 4], [10, 0]])
+    kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
+    print(f"kmeans.labels_ = {kmeans.labels_}")
+
+
+Global Patching
+**********************
+
+You can also use global patching to patch all your scikit-learn applications without any additional actions.
+
+Before you begin, make sure that you have read and write permissions for Scikit-learn files. 
+
+With global patching, you can:
+
+.. list-table:: 
+   :header-rows: 1
+   :align: left
+
+   * - Task
+     - Action
+     - Note
+   * - Patch all supported algorithms
+     - Run this command:
+
+       :: 
+         
+          python -m sklearnex.glob patch_sklearn
+     
+     - If you run the global patching command several times with different parameters, then only the last configuration is applied.
+   * - Patch selected algorithms
+     - Use ``--algorithm`` or ``-a`` keys with a list of algorithms to patch. For example, to patch only ``SVC`` and ``RandomForestClassifier`` estimators, run
+
+       ::
+ 
+           python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier
+  
+     -   
+   * - Enable global patching via code
+     - Use the ``patch_sklearn`` function with the ``global_patch`` argument:
+
+       ::
+
+          from sklearnex import patch_sklearn
+          patch_sklearn(global_patch=True)
+          import sklearn
+      
+     - After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.
+   * - Disable patching notifications
+     - Use ``--no-verbose`` or ``-nv`` keys:
+
+       ::
+
+          python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier -nv
+     -  
+   * - Disable global patching
+     - Run this command:
+
+       ::
+
+          python -m sklearnex.glob unpatch_sklearn
+     -
+   * - Disable global patching via code
+     - Use the ``global_patch`` argument in the ``unpatch_sklearn`` function
+
+       ::
+
+          from sklearnex import unpatch_sklearn
+          unpatch_sklearn(global_patch=True)
+     -
+    
+.. tip:: If you clone an environment with enabled global patching, it will already be applied in the new environment.
+
+Unpatching
+**********************
+
+To undo the patch (also called `unpatching`) is to return scikit-learn to original implementation and
+replace patched algorithms with the stock scikit-learn algorithms.
+
+To unpatch successfully, you must reimport the scikit-learn package::
+
+  sklearnex.unpatch_sklearn()
+  # Re-import scikit-learn algorithms after the unpatch
+  from sklearn.cluster import KMeans  
+
+
+Installation 
+--------------------
+
+.. contents:: :local:
+
+.. tip:: To prevent version conflicts, we recommend creating and activating a new environment for |intelex|. 
+
+Install from PyPI 
+**********************
+
+Recommended by default. 
+
+To install |intelex|, run:
+
+::
+
+  pip install scikit-learn-intelex
+
+**Supported Configurations**
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+
+   * - OS / Python version
+     - Python 3.9
+     - Python 3.10
+     - Python 3.11
+     - Python 3.12
+   * - Linux* OS
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+   * - Windows* OS
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+
+
+
+Install from Anaconda* Cloud
+********************************************
+
+To prevent version conflicts, we recommend installing `scikit-learn-intelex` into a new conda environment.
+
+.. tabs::
+
+   .. tab:: Conda-Forge channel
+
+      Recommended by default. 
+      
+      To install, run::
+
+        conda install scikit-learn-intelex -c conda-forge
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+         * - Windows* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+
+
+   .. tab:: Intel channel
+
+      Recommended for the Intel® Distribution for Python users. 
+
+      To install, run::
+
+        conda install scikit-learn-intelex -c https://software.repos.intel.com/python/conda/
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+         * - Windows* OS
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+ 
+
+
+   .. tab:: Main channel
+
+      To install, run::
+
+        conda install scikit-learn-intelex
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+         * - Windows* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+
+
+
+Build from Sources
+**********************
+
+See `Installation instructions <https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md>`_ to build |intelex| from the sources.
+
+Install Intel*(R) AI Tools
+****************************
+
+Download the Intel AI Tools `here <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html>`_. The extension is already included.
+
+Release Notes
+-------------------
+
+See the `Release Notes <https://github.com/intel/scikit-learn-intelex/releases>`_ for each version of Intel® Extension for Scikit-learn*.  
+
+System Requirements 
+--------------------
+
+Hardware Requirements
+**********************
+
+.. tabs::
+
+   .. tab:: CPU
+
+      All processors with ``x86`` architecture with at least one of the following instruction sets:
+
+        - SSE2
+        - SSE4.2
+        - AVX2
+        - AVX512
+       
+      .. note:: ARM* architecture is not supported.
+
+   .. tab:: GPU
+
+      - All Intel® integrated and discrete GPUs
+      - Intel® GPU drivers
+
+
+.. tip:: Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our :ref:`blogs <blogs>`.
+
+
+Software Requirements
+**********************
+
+.. tabs::
+
+   .. tab:: CPU
+
+      - Linux* OS: Ubuntu* 18.04 or newer
+      - Windows* OS 10 or newer
+      - Windows* Server 2019 or newer
+
+   .. tab:: GPU
+
+      - Linux* OS: Ubuntu* 18.04 or newer
+      - Windows* OS 10 or newer
+      - Windows* Server 2019 or newer
+      
+      .. important::
+         
+         If you use accelerators, refer to `oneAPI DPC++/C++ Compiler System Requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_.
+
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:
+
+* 1.0.X
+* 1.1.X
+* 1.2.X 
+* 1.3.X
+
+Memory Requirements
+**********************
+By default, algorithms in |intelex| run in the multi-thread mode. This mode uses all available threads. 
+Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Single-thread mode
+     - Multi-thread mode
+   * - SVM
+     - Both Scikit-learn and |intelex| consume approximately the same amount of RAM.
+     - In |intelex|, an algorithm with ``N`` threads consumes ``N`` times more RAM.
+
+In all |intelex| algorithms with GPU support, computations run on device memory. 
+The device memory must be large enough to store a copy of the entire dataset.
+You may also require additional device memory for internal arrays that are used in computation.
+
+
+.. seealso::
+
+   :ref:`Samples<samples>`
diff --git a/2024.7/_sources/samples.rst.txt b/2024.7/_sources/samples.rst.txt
new file mode 100644
index 0000000000..e616e8f2d9
--- /dev/null
+++ b/2024.7/_sources/samples.rst.txt
@@ -0,0 +1,68 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _samples:
+
+#######
+Samples 
+#######
+
+The following samples are also provided as Jupyter notebooks in |intelex| repository.
+If you want to run them locally, refer to `these instructions
+<https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks>`_.
+
+.. rubric:: Classification Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   k-Nearest Neighbors (kNN) for MNIST dataset <samples/knn_mnist.ipynb>
+   Logistic Regression for Cifar dataset <samples/logistictic_regression_cifar.ipynb>
+   Support Vector Classification (SVC) for Adult dataset <samples/svc_adult.ipynb>
+
+.. rubric:: Regression Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   ElasticNet for Airlines DepDelay dataset <samples/ElasticNet.ipynb>
+   Lasso Regression for YearPredictionMSD dataset <samples/lasso_regression.ipynb>
+   Linear Regression for YearPredictionMSD dataset <samples/linear_regression.ipynb>
+   Nu-Support Vector Regression (NuSVR) for Medical Charges dataset <samples/nusvr_medical_charges.ipynb>
+   Random Forest for Yolanda dataset <samples/random_forest_yolanda.ipynb>
+   Rigde Regression for Airlines DepDelay dataset <samples/ridge_regression.ipynb>
+
+.. rubric:: Clustering Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   Kmeans for spoken arabic digit dataset <samples/kmeans.ipynb>
+   DBSCAN for spoken arabic digit dataset <samples/dbscan.ipynb>
+
+.. rubric:: Jupyter Notebook Samples
+
+.. toctree::
+   :maxdepth: 1
+
+   Intel® Extension for Scikit-Learn* Getting Started Sample <https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted>
+   Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample <https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult>
+
+.. seealso::
+
+   There are also :ref:`Kaggle kernels <kaggle>` that use |intelex| for a variety of machine learning scenarios.
+
+.. note:: Explore the complete list of oneAPI code samples in the `oneAPI Samples Catalog <https://oneapi-src.github.io/oneAPI-samples/>`_. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.
diff --git a/2024.7/_sources/samples/ElasticNet.ipynb.txt b/2024.7/_sources/samples/ElasticNet.ipynb.txt
new file mode 100644
index 0000000000..29e5dca851
--- /dev/null
+++ b/2024.7/_sources/samples/ElasticNet.ipynb.txt
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/daal4py_data_science.ipynb.txt b/2024.7/_sources/samples/daal4py_data_science.ipynb.txt
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2024.7/_sources/samples/daal4py_data_science.ipynb.txt
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2024.7/_sources/samples/dbscan.ipynb.txt b/2024.7/_sources/samples/dbscan.ipynb.txt
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2024.7/_sources/samples/dbscan.ipynb.txt
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/kmeans.ipynb.txt b/2024.7/_sources/samples/kmeans.ipynb.txt
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2024.7/_sources/samples/kmeans.ipynb.txt
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/knn_mnist.ipynb.txt b/2024.7/_sources/samples/knn_mnist.ipynb.txt
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2024.7/_sources/samples/knn_mnist.ipynb.txt
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/lasso_regression.ipynb.txt b/2024.7/_sources/samples/lasso_regression.ipynb.txt
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2024.7/_sources/samples/lasso_regression.ipynb.txt
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/linear_regression.ipynb.txt b/2024.7/_sources/samples/linear_regression.ipynb.txt
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2024.7/_sources/samples/linear_regression.ipynb.txt
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/logistictic_regression_cifar.ipynb.txt b/2024.7/_sources/samples/logistictic_regression_cifar.ipynb.txt
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2024.7/_sources/samples/logistictic_regression_cifar.ipynb.txt
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/nusvr_medical_charges.ipynb.txt b/2024.7/_sources/samples/nusvr_medical_charges.ipynb.txt
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2024.7/_sources/samples/nusvr_medical_charges.ipynb.txt
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/random_forest_yolanda.ipynb.txt b/2024.7/_sources/samples/random_forest_yolanda.ipynb.txt
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2024.7/_sources/samples/random_forest_yolanda.ipynb.txt
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/ridge_regression.ipynb.txt b/2024.7/_sources/samples/ridge_regression.ipynb.txt
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2024.7/_sources/samples/ridge_regression.ipynb.txt
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/svc_adult.ipynb.txt b/2024.7/_sources/samples/svc_adult.ipynb.txt
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2024.7/_sources/samples/svc_adult.ipynb.txt
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/samples/tsne.ipynb.txt b/2024.7/_sources/samples/tsne.ipynb.txt
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2024.7/_sources/samples/tsne.ipynb.txt
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/_sources/support.rst.txt b/2024.7/_sources/support.rst.txt
new file mode 100644
index 0000000000..03a2dddadb
--- /dev/null
+++ b/2024.7/_sources/support.rst.txt
@@ -0,0 +1,34 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+######################################################
+Intel(R) Extension for Scikit-learn Support
+######################################################
+
+
+We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn. 
+
+Use the following methods if you face any challenges. 
+
+
+Issues
+----------------------------------
+
+If you have a problem, check out the `GitHub Issues <https://github.com/intel/scikit-learn-intelex/issues>`_ to see if the issue you want to address is already reported. 
+
+You may find users that have encountered the same bug or have similar ideas for changes or updates.
+
+You can use issues to report a problem, make a feature request, or add comments on an existing issue.
diff --git a/2024.3/_sources/tutorials.rst.txt b/2024.7/_sources/tutorials.rst.txt
similarity index 100%
rename from 2024.3/_sources/tutorials.rst.txt
rename to 2024.7/_sources/tutorials.rst.txt
diff --git a/2024.7/_sources/verbose.rst.txt b/2024.7/_sources/verbose.rst.txt
new file mode 100644
index 0000000000..cf77cc0669
--- /dev/null
+++ b/2024.7/_sources/verbose.rst.txt
@@ -0,0 +1,54 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _verbose:
+
+############
+Verbose Mode
+############
+
+Use |intelex| in verbose mode to find out which implementation of the algorithm is currently used,
+|intelex| or original Scikit-learn.
+
+.. note:: Verbose mode is only available for :ref:`supported algorithms <sklearn_algorithms>`.
+
+To enable verbose mode, set the ``SKLEARNEX_VERBOSE`` environment variable as shown below:
+
+- On Linux* OS ::
+
+     export SKLEARNEX_VERBOSE=INFO
+
+- On Windows* OS ::
+
+     set SKLEARNEX_VERBOSE=INFO
+
+Alternatively, get |intelex| logger and set its logging level in the Python code::
+
+     import logging
+     logger = logging.getLogger('sklearnex')
+     logger.setLevel(logging.INFO)
+
+During the calls that use Intel-optimized scikit-learn, you will receive additional print statements
+that indicate which implementation is being called.
+These print statements are only available for :ref:`supported algorithms <sklearn_algorithms>`.
+
+For example, for DBSCAN you get one of these print statements depending on which implementation is used::
+
+    INFO:sklearnex: sklearn.cluster.DBSCAN.fit: running accelerated version on CPU
+
+::
+
+    INFO:sklearnex: sklearn.cluster.DBSCAN.fit: fallback to original Scikit-learn
diff --git a/2024.3/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/2024.7/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
similarity index 100%
rename from 2024.3/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
rename to 2024.7/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
diff --git a/2024.3/_sphinx_design_static/design-tabs.js b/2024.7/_sphinx_design_static/design-tabs.js
similarity index 100%
rename from 2024.3/_sphinx_design_static/design-tabs.js
rename to 2024.7/_sphinx_design_static/design-tabs.js
diff --git a/2024.7/_static/_sphinx_javascript_frameworks_compat.js b/2024.7/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000000..81415803ec
--- /dev/null
+++ b/2024.7/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/2024.7/_static/basic.css b/2024.7/_static/basic.css
new file mode 100644
index 0000000000..7577acb1ad
--- /dev/null
+++ b/2024.7/_static/basic.css
@@ -0,0 +1,903 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
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diff --git a/latest/_static/style.css b/2024.7/_static/custom.css
similarity index 89%
rename from latest/_static/style.css
rename to 2024.7/_static/custom.css
index b631dcb91b..1db55bd294 100644
--- a/latest/_static/style.css
+++ b/2024.7/_static/custom.css
@@ -120,3 +120,12 @@ div.quotation {
 }
 
 a#wap_dns {display: none;}
+
+
+button#version-switcher-button {
+   background-color:  #2980b9;
+}
+
+div#version-switcher-dropdown {
+   display: inline;
+}
diff --git a/2024.3/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/2024.7/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
similarity index 100%
rename from 2024.3/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
rename to 2024.7/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
diff --git a/2024.3/_static/design-tabs.js b/2024.7/_static/design-tabs.js
similarity index 100%
rename from 2024.3/_static/design-tabs.js
rename to 2024.7/_static/design-tabs.js
diff --git a/2024.7/_static/doctools.js b/2024.7/_static/doctools.js
new file mode 100644
index 0000000000..d06a71d751
--- /dev/null
+++ b/2024.7/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/2024.7/_static/documentation_options.js b/2024.7/_static/documentation_options.js
new file mode 100644
index 0000000000..6284f5d905
--- /dev/null
+++ b/2024.7/_static/documentation_options.js
@@ -0,0 +1,14 @@
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+    VERSION: '2024.7',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
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diff --git a/2024.7/_static/js/badge_only.js b/2024.7/_static/js/badge_only.js
new file mode 100644
index 0000000000..526d7234b6
--- /dev/null
+++ b/2024.7/_static/js/badge_only.js
@@ -0,0 +1 @@
+!function(e){var t={};function r(n){if(t[n])return t[n].exports;var o=t[n]={i:n,l:!1,exports:{}};return e[n].call(o.exports,o,o.exports,r),o.l=!0,o.exports}r.m=e,r.c=t,r.d=function(e,t,n){r.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},r.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(r.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)r.d(n,o,function(t){return e[t]}.bind(null,o));return n},r.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return r.d(t,"a",t),t},r.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},r.p="",r(r.s=4)}({4:function(e,t,r){}});
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diff --git a/_scripts/theme.js b/2024.7/_static/js/theme.js
similarity index 100%
rename from _scripts/theme.js
rename to 2024.7/_static/js/theme.js
diff --git a/2024.7/_static/js/versions.js b/2024.7/_static/js/versions.js
new file mode 100644
index 0000000000..818bc9969f
--- /dev/null
+++ b/2024.7/_static/js/versions.js
@@ -0,0 +1,224 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = "True";
+const themeLanguageSelector = "True";
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${config.projects.translations
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/2024.7/_static/language_data.js b/2024.7/_static/language_data.js
new file mode 100644
index 0000000000..250f5665fa
--- /dev/null
+++ b/2024.7/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
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new file mode 100644
index 0000000000..4919ca8829
--- /dev/null
+++ b/2024.7/_static/nbsphinx-broken-thumbnail.svg
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+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
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diff --git a/2024.7/_static/nbsphinx-code-cells.css b/2024.7/_static/nbsphinx-code-cells.css
new file mode 100644
index 0000000000..a3fb27c30f
--- /dev/null
+++ b/2024.7/_static/nbsphinx-code-cells.css
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+/* remove conflicting styling from Sphinx themes */
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+    background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
+    }
+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
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+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
+}
+
+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+    color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+    color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+    width: 4.5ex;
+    padding-top: 5px;
+    position: relative;
+    user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+    position: absolute;
+    right: 0;
+    margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+    div.nbinput.container div.prompt,
+    div.nboutput.container div.prompt {
+        width: unset;
+        text-align: left;
+        padding: 0.4em;
+    }
+    div.nboutput.container div.prompt.empty {
+        padding: 0;
+    }
+
+    div.nbinput.container div.prompt > div,
+    div.nboutput.container div.prompt > div {
+        position: unset;
+    }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+    overflow: hidden;
+    white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+    -webkit-flex: 1;
+    flex: 1;
+    overflow: auto;
+}
+@media (max-width: 540px) {
+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
+        width: 100%;
+    }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+    border: 1px solid #e0e0e0;
+    border-radius: 2px;
+    /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
+    text-align: left !important;
+}
+
+/* override sphinx.ext.imgmath center alignment in output cells */
+div.nboutput.container div.math p {
+    text-align: left;
+}
+
+/* standard error */
+div.nboutput.container div.output_area.stderr {
+    background: #fdd;
+}
+
+/* ANSI colors */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
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+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-default-inverse-fg { color: #FFFFFF; }
+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+  font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
diff --git a/2024.7/_static/nbsphinx-gallery.css b/2024.7/_static/nbsphinx-gallery.css
new file mode 100644
index 0000000000..365c27a96b
--- /dev/null
+++ b/2024.7/_static/nbsphinx-gallery.css
@@ -0,0 +1,31 @@
+.nbsphinx-gallery {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 5px;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+    padding: 5px;
+    border: 1px dotted currentColor;
+    border-radius: 2px;
+    text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+    border-style: solid;
+}
+
+.nbsphinx-gallery img {
+    max-width: 100%;
+    max-height: 100%;
+}
+
+.nbsphinx-gallery > a > div:first-child {
+    display: flex;
+    align-items: start;
+    justify-content: center;
+    height: 120px;
+    margin-bottom: 5px;
+}
diff --git a/2024.7/_static/nbsphinx-no-thumbnail.svg b/2024.7/_static/nbsphinx-no-thumbnail.svg
new file mode 100644
index 0000000000..9dca7588fa
--- /dev/null
+++ b/2024.7/_static/nbsphinx-no-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: #ccc; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: #999; }
+    }
+  </style>
+  <path d="M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 H 10.915948 Z M 65.497127,39.809967 q -2.34835,0 -3.736011,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010157 1.376987,4.760745 1.387661,1.750588 3.736011,1.750588 2.348349,0 3.714662,-1.750588 1.376986,-1.750588 1.376986,-4.760745 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714662,-1.750588 z m 0,-1.750588 q 3.351735,0 5.358506,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358506,2.241606 -3.36241,0 -5.379856,-2.241606 -2.006771,-2.241607 -2.006771,-6.020315 0,-3.778708 2.006771,-6.020314 2.017446,-2.252281 5.379856,-2.252281 z m -22.821688,0.288206 h 2.903414 L 52.64525,51.679807 V 38.347585 h 2.092166 V 54.28434 H 51.834001 L 44.767605,40.952119 V 54.28434 h -2.092166 z" />
+</svg>
diff --git a/2024.7/_static/oneAPI-rgb-rev-100.png b/2024.7/_static/oneAPI-rgb-rev-100.png
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diff --git a/2024.7/_static/pygments.css b/2024.7/_static/pygments.css
new file mode 100644
index 0000000000..0d49244eda
--- /dev/null
+++ b/2024.7/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #eeffcc; }
+.highlight .c { color: #408090; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #007020; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #007020 } /* Comment.Preproc */
+.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
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+.highlight .nv { color: #bb60d5 } /* Name.Variable */
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diff --git a/2024.7/_static/scikit-learn-acceleration-2021.2.3.PNG b/2024.7/_static/scikit-learn-acceleration-2021.2.3.PNG
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diff --git a/2024.7/_static/scikit-learn-acceleration.PNG b/2024.7/_static/scikit-learn-acceleration.PNG
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index 0000000000..d045478f39
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diff --git a/2024.7/_static/searchtools.js b/2024.7/_static/searchtools.js
new file mode 100644
index 0000000000..97d56a74d8
--- /dev/null
+++ b/2024.7/_static/searchtools.js
@@ -0,0 +1,566 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = docUrlRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = docUrlRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr)
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms)
+          );
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent !== undefined) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/2024.7/_static/sphinx_highlight.js b/2024.7/_static/sphinx_highlight.js
new file mode 100644
index 0000000000..aae669d7ea
--- /dev/null
+++ b/2024.7/_static/sphinx_highlight.js
@@ -0,0 +1,144 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(SphinxHighlight.highlightSearchWords);
+_ready(SphinxHighlight.initEscapeListener);
diff --git a/2024.7/_static/tabs.css b/2024.7/_static/tabs.css
new file mode 100644
index 0000000000..957ba60d69
--- /dev/null
+++ b/2024.7/_static/tabs.css
@@ -0,0 +1,89 @@
+.sphinx-tabs {
+  margin-bottom: 1rem;
+}
+
+[role="tablist"] {
+  border-bottom: 1px solid #a0b3bf;
+}
+
+.sphinx-tabs-tab {
+  position: relative;
+  font-family: Lato,'Helvetica Neue',Arial,Helvetica,sans-serif;
+  color: #1D5C87;
+  line-height: 24px;
+  margin: 0;
+  font-size: 16px;
+  font-weight: 400;
+  background-color: rgba(255, 255, 255, 0);
+  border-radius: 5px 5px 0 0;
+  border: 0;
+  padding: 1rem 1.5rem;
+  margin-bottom: 0;
+}
+
+.sphinx-tabs-tab[aria-selected="true"] {
+  font-weight: 700;
+  border: 1px solid #a0b3bf;
+  border-bottom: 1px solid white;
+  margin: -1px;
+  background-color: white;
+}
+
+.sphinx-tabs-tab:focus {
+  z-index: 1;
+  outline-offset: 1px;
+}
+
+.sphinx-tabs-panel {
+  position: relative;
+  padding: 1rem;
+  border: 1px solid #a0b3bf;
+  margin: 0px -1px -1px -1px;
+  border-radius: 0 0 5px 5px;
+  border-top: 0;
+  background: white;
+}
+
+.sphinx-tabs-panel.code-tab {
+  padding: 0.4rem;
+}
+
+.sphinx-tab img {
+	margin-bottom: 24 px;
+}
+
+/* Dark theme preference styling */
+
+@media (prefers-color-scheme: dark) {
+  body[data-theme="auto"] .sphinx-tabs-panel {
+    color: white;
+    background-color: rgb(50, 50, 50);
+  }
+
+  body[data-theme="auto"] .sphinx-tabs-tab {
+    color: white;
+    background-color: rgba(255, 255, 255, 0.05);
+  }
+
+  body[data-theme="auto"] .sphinx-tabs-tab[aria-selected="true"] {
+    border-bottom: 1px solid rgb(50, 50, 50);
+    background-color: rgb(50, 50, 50);
+  }
+}
+
+/* Explicit dark theme styling */
+
+body[data-theme="dark"] .sphinx-tabs-panel {
+  color: white;
+  background-color: rgb(50, 50, 50);
+}
+
+body[data-theme="dark"] .sphinx-tabs-tab {
+  color: white;
+  background-color: rgba(255, 255, 255, 0.05);
+}
+
+body[data-theme="dark"] .sphinx-tabs-tab[aria-selected="true"] {
+  border-bottom: 2px solid rgb(50, 50, 50);
+  background-color: rgb(50, 50, 50);
+}
diff --git a/2024.7/_static/tabs.js b/2024.7/_static/tabs.js
new file mode 100644
index 0000000000..48dc303c8c
--- /dev/null
+++ b/2024.7/_static/tabs.js
@@ -0,0 +1,145 @@
+try {
+  var session = window.sessionStorage || {};
+} catch (e) {
+  var session = {};
+}
+
+window.addEventListener("DOMContentLoaded", () => {
+  const allTabs = document.querySelectorAll('.sphinx-tabs-tab');
+  const tabLists = document.querySelectorAll('[role="tablist"]');
+
+  allTabs.forEach(tab => {
+    tab.addEventListener("click", changeTabs);
+  });
+
+  tabLists.forEach(tabList => {
+    tabList.addEventListener("keydown", keyTabs);
+  });
+
+  // Restore group tab selection from session
+  const lastSelected = session.getItem('sphinx-tabs-last-selected');
+  if (lastSelected != null) selectNamedTabs(lastSelected);
+});
+
+/**
+ * Key focus left and right between sibling elements using arrows
+ * @param  {Node} e the element in focus when key was pressed
+ */
+function keyTabs(e) {
+    const tab = e.target;
+    let nextTab = null;
+    if (e.keyCode === 39 || e.keyCode === 37) {
+      tab.setAttribute("tabindex", -1);
+      // Move right
+      if (e.keyCode === 39) {
+        nextTab = tab.nextElementSibling;
+        if (nextTab === null) {
+          nextTab = tab.parentNode.firstElementChild;
+        }
+      // Move left
+      } else if (e.keyCode === 37) {
+        nextTab = tab.previousElementSibling;
+        if (nextTab === null) {
+          nextTab = tab.parentNode.lastElementChild;
+        }
+      }
+    }
+
+    if (nextTab !== null) {
+      nextTab.setAttribute("tabindex", 0);
+      nextTab.focus();
+    }
+}
+
+/**
+ * Select or deselect clicked tab. If a group tab
+ * is selected, also select tab in other tabLists.
+ * @param  {Node} e the element that was clicked
+ */
+function changeTabs(e) {
+  // Use this instead of the element that was clicked, in case it's a child
+  const notSelected = this.getAttribute("aria-selected") === "false";
+  const positionBefore = this.parentNode.getBoundingClientRect().top;
+  const notClosable = !this.parentNode.classList.contains("closeable");
+
+  deselectTabList(this);
+
+  if (notSelected || notClosable) {
+    selectTab(this);
+    const name = this.getAttribute("name");
+    selectNamedTabs(name, this.id);
+
+    if (this.classList.contains("group-tab")) {
+      // Persist during session
+      session.setItem('sphinx-tabs-last-selected', name);
+    }
+  }
+
+  const positionAfter = this.parentNode.getBoundingClientRect().top;
+  const positionDelta = positionAfter - positionBefore;
+  // Scroll to offset content resizing
+  window.scrollTo(0, window.scrollY + positionDelta);
+}
+
+/**
+ * Select tab and show associated panel.
+ * @param  {Node} tab tab to select
+ */
+function selectTab(tab) {
+  tab.setAttribute("aria-selected", true);
+
+  // Show the associated panel
+  document
+    .getElementById(tab.getAttribute("aria-controls"))
+    .removeAttribute("hidden");
+}
+
+/**
+ * Hide the panels associated with all tabs within the
+ * tablist containing this tab.
+ * @param  {Node} tab a tab within the tablist to deselect
+ */
+function deselectTabList(tab) {
+  const parent = tab.parentNode;
+  const grandparent = parent.parentNode;
+
+  Array.from(parent.children)
+  .forEach(t => t.setAttribute("aria-selected", false));
+
+  Array.from(grandparent.children)
+    .slice(1)  // Skip tablist
+    .forEach(panel => panel.setAttribute("hidden", true));
+}
+
+/**
+ * Select grouped tabs with the same name, but no the tab
+ * with the given id.
+ * @param  {Node} name name of grouped tab to be selected
+ * @param  {Node} clickedId id of clicked tab
+ */
+function selectNamedTabs(name, clickedId=null) {
+  const groupedTabs = document.querySelectorAll(`.sphinx-tabs-tab[name="${name}"]`);
+  const tabLists = Array.from(groupedTabs).map(tab => tab.parentNode);
+
+  tabLists
+    .forEach(tabList => {
+      // Don't want to change the tabList containing the clicked tab
+      const clickedTab = tabList.querySelector(`[id="${clickedId}"]`);
+      if (clickedTab === null ) {
+        // Select first tab with matching name
+        const tab = tabList.querySelector(`.sphinx-tabs-tab[name="${name}"]`);
+        deselectTabList(tab);
+        selectTab(tab);
+      }
+    })
+}
+
+if (typeof exports === 'undefined') {
+  exports = {};
+}
+
+exports.keyTabs = keyTabs;
+exports.changeTabs = changeTabs;
+exports.selectTab = selectTab;
+exports.deselectTabList = deselectTabList;
+exports.selectNamedTabs = selectNamedTabs;
diff --git a/2024.7/_static/version_switcher.js b/2024.7/_static/version_switcher.js
new file mode 100644
index 0000000000..162851d217
--- /dev/null
+++ b/2024.7/_static/version_switcher.js
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+function load_versions(json){
+    var button = document.getElementById('version-switcher-button')
+    var container = document.getElementById('version-switcher-dropdown')
+    var loc = window.location.href;
+    var s = document.createElement('select');
+    s.style = "border-radius:5px;"
+    const versions = JSON.parse(json);
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+    });
+    container.append(s);
+}
diff --git a/2024.7/algorithms.html b/2024.7/algorithms.html
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+++ b/2024.7/algorithms.html
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+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+  <title>Supported Algorithms &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
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+            Intel(R) Extension for Scikit-learn*
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+             
+  <section id="supported-algorithms">
+<span id="sklearn-algorithms"></span><h1>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Permalink to this heading"></a></h1>
+<p>Applying Intel® Extension for Scikit-learn* impacts the following scikit-learn algorithms:</p>
+<section id="on-cpu">
+<h2>on CPU<a class="headerlink" href="#on-cpu" title="Permalink to this heading"></a></h2>
+<section id="classification">
+<h3>Classification<a class="headerlink" href="#classification" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>SVC</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>NuSVC</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>RandomForestClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">cpp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsClassifier</cite></p></td>
+<td><ul>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == <cite>‘kd_tree’</cite>:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p>
+</li>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == <cite>‘brute’</cite>:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p>
+</li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LogisticRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> not in [<cite>‘lbfgs’</cite>, <cite>‘newton-cg’</cite>]</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="regression">
+<h3>Regression<a class="headerlink" href="#regression" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>SVR</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>NuSVR</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>RandomForestRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">cpp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LinearRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>Ridge</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> != <cite>‘auto’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>ElasticNet</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>Lasso</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="clustering">
+<h3>Clustering<a class="headerlink" href="#clustering" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>KMeans</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>DBSCAN</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> not in [<cite>‘brute’</cite>, <cite>‘auto’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="dimensionality-reduction">
+<h3>Dimensionality reduction<a class="headerlink" href="#dimensionality-reduction" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>PCA</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>TSNE</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != ‘euclidean’ or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+<p>Refer to <a class="reference internal" href="guide/acceleration.html#acceleration-tsne"><span class="std std-ref">TSNE acceleration details</span></a> to learn more.</p>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="nearest-neighbors">
+<h3>Nearest Neighbors<a class="headerlink" href="#nearest-neighbors" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>NearestNeighbors</cite></p></td>
+<td><ul>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == ‘kd_tree’:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p>
+</li>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == ‘brute’:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p>
+</li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="other-tasks">
+<h3>Other tasks<a class="headerlink" href="#other-tasks" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>train_test_split</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>assert_all_finite</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>pairwise_distance</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘cosine’</cite>, <cite>‘correlation’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>roc_auc_score</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">average</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">max_fpr</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">multi_class</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>No limitations</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="on-gpu">
+<h2>on GPU<a class="headerlink" href="#on-gpu" title="Permalink to this heading"></a></h2>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="oneapi-gpu.html#oneapi-gpu"><span class="std std-ref">oneAPI and GPU support in Intel® Extension for Scikit-learn*</span></a></p>
+</div>
+<section id="id1">
+<h3>Classification<a class="headerlink" href="#id1" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>SVC</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">kernel</span></code> = <cite>‘sigmoid_poly’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only binary dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>RandomForestClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">cpp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>KNeighborsClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>LogisticRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> != <cite>‘newton-cg’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">penalty</span></code> != <cite>‘l2’</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id2">
+<h3>Regression<a class="headerlink" href="#id2" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>RandomForestRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">cpp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LinearRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id3">
+<h3>Clustering<a class="headerlink" href="#id3" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>KMeans</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+<p><code class="docutils literal notranslate"><span class="pre">Init</span></code> = <cite>‘k-means++’</cite> fallbacks to CPU.</p>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>DBSCAN</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> not in [<cite>‘brute’</cite>, <cite>‘auto’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id4">
+<h3>Dimensionality reduction<a class="headerlink" href="#id4" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>PCA</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id5">
+<h3>Nearest Neighbors<a class="headerlink" href="#id5" title="Permalink to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 50%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>NearestNeighbors</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="scikit-learn-tests">
+<h2>Scikit-learn tests<a class="headerlink" href="#scikit-learn-tests" title="Permalink to this heading"></a></h2>
+<p>Monkey-patched scikit-learn classes and functions passes scikit-learn’s own test
+suite, with few exceptions, specified in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/deselected_tests.yaml">deselected_tests.yaml</a>.</p>
+<p>The results of the entire latest scikit-learn test suite with Intel® Extension for Scikit-learn*: <a class="reference external" href="https://circleci.com/gh/intel/scikit-learn-intelex">CircleCI</a>.</p>
+</section>
+</section>
+
+
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
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+<ul class="simple">
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/save-time-and-money-with-intel-extension-for-scikit-learn-33627425ae4">Save Time and Money with Intel Extension for Scikit-learn</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/superior-machine-learning-performance-on-the-latest-intel-xeon-scalable-processor-efdec279f5a3">Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/leverage-intel-optimizations-in-scikit-learn-f562cb9d5544">Leverage Intel Optimizations in Scikit-Learn</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/intel-gives-scikit-learn-the-performance-boost-data-scientists-need-42eb47c80b18">Intel Gives Scikit-Learn the Performance Boost Data Scientists Need</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae">From Hours to Minutes: 600x Faster SVM</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e">Improve the Performance of XGBoost and LightGBM Inference</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a">Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e">Accelerate Your scikit-learn Applications</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe">Accelerate Linear Models for Machine Learning</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1">Accelerate K-Means Clustering</a>.</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/why-pay-more-for-machine-learning-893683bd78e4">Why Pay More for Machine Learning?</a>.</p></li>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
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+  <ul class="wy-breadcrumbs">
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+      <li class="breadcrumb-item active">How to Contribute</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/contribute.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="how-to-contribute">
+<h1>How to Contribute<a class="headerlink" href="#how-to-contribute" title="Permalink to this heading"></a></h1>
+<p>As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn.
+This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.</p>
+<section id="licensing">
+<h2>Licensing<a class="headerlink" href="#licensing" title="Permalink to this heading"></a></h2>
+<p>Intel(R) Extension for Scikit-learn uses the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE">Apache 2.0 License</a>. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.</p>
+</section>
+<section id="pull-requests">
+<h2>Pull Requests<a class="headerlink" href="#pull-requests" title="Permalink to this heading"></a></h2>
+<p>No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.</p>
+<p>Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">name</span> <span class="s2">&quot;Kate Developer&quot;</span>
+<span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">email</span> <span class="n">kate</span><span class="o">.</span><span class="n">developer</span><span class="nd">@company</span><span class="o">.</span><span class="n">com</span>
+</pre></div>
+</div>
+<section id="before-contributing-changes">
+<h3>Before Contributing Changes<a class="headerlink" href="#before-contributing-changes" title="Permalink to this heading"></a></h3>
+<p>Make sure you can build the product and run all the tests with your patch.
+For a larger feature, provide a relevant test.
+Document your code. Our project uses reStructuredText for documentation.
+For new file(s), specify the appropriate copyright year in the first line.
+Submit a pull request into the master branch.
+Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.</p>
+</section>
+</section>
+<section id="code-style">
+<h2>Code Style<a class="headerlink" href="#code-style" title="Permalink to this heading"></a></h2>
+<p>We use <a class="reference external" href="https://black.readthedocs.io/en/stable/">black</a> and <a class="reference external" href="https://pycqa.github.io/isort/">isort</a> formatters for Python* code.
+The line length is 90 characters; use default options otherwise. You can find the linter configuration in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml">.pyproject.toml</a>.</p>
+<p>A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.</p>
+<p>Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pre</span><span class="o">-</span><span class="n">commit</span>
+<span class="n">pre</span><span class="o">-</span><span class="n">commit</span> <span class="n">install</span>
+</pre></div>
+</div>
+</section>
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diff --git a/2024.7/deprecation.html b/2024.7/deprecation.html
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+            Intel(R) Extension for Scikit-learn*
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+    >
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+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
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+<p>This page provides information about the deprecations of a specific Intel® Extension for Scikit-learn* functionality.</p>
+<section id="macos-support">
+<h2>macOS* Support<a class="headerlink" href="#macos-support" title="Permalink to this heading"></a></h2>
+<p><strong>Deprecation:</strong> macOS* support is deprecated for Intel® Extension for Scikit-learn*. The 2023.x releases are the last to provide it.</p>
+<p><strong>Reasons for deprecation:</strong> No modern X86 macOS*-based systems are to be released.</p>
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
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+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
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+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<p class="admonition-title">Note</p>
+<p>Intel® Extension for Scikit-learn* contains scikit-learn patching functionality that was originally available in
+<a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/daal4py">daal4py</a> package.
+We recommend you to use scikit-learn-intelex package instead of daal4py.
+You can learn more about daal4py in <a class="reference external" href="https://intelpython.github.io/daal4py">daal4py documentation</a>.</p>
+</div>
+<p>While daal4py is available in <a class="reference external" href="https://intelpython.github.io/daal4py/scaling.html">distribued mode</a>,
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
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+</li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
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+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="tuning-guide">
+<h1>Tuning Guide<a class="headerlink" href="#tuning-guide" title="Permalink to this heading"></a></h1>
+<p>The performance of some algorithms changes based on the parameters that are used.
+This section denotes the details of such cases.</p>
+<p>Refer to <a class="reference internal" href="../algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a> to see the full list of algorithms, parameters, and data formats supported in Intel® Extension for Scikit-learn*.</p>
+<section id="tsne">
+<span id="acceleration-tsne"></span><h2>TSNE<a class="headerlink" href="#tsne" title="Permalink to this heading"></a></h2>
+<p>TSNE algorithm consists of two components: KNN and Gradient Descent.
+The overall acceleration of TSNE depends on the acceleration of each of these algorithms.</p>
+<ul class="simple">
+<li><p>The KNN part of the algorithm supports all parameters except:</p>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</li>
+<li><p>The Gradient Descent part of the algorithm supports all parameters except:</p>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">n_components</span></code> = <cite>3</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">method</span></code> = <cite>‘exact’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">verbose</span></code> != <cite>0</cite></p></li>
+</ul>
+</li>
+</ul>
+<p>To get better performance, use parameters supported by both components.</p>
+</section>
+<section id="random-forest">
+<span id="acceleration-rf"></span><h2>Random Forest<a class="headerlink" href="#random-forest" title="Permalink to this heading"></a></h2>
+<p>Random Forest models accelerated with Intel® Extension for Scikit-learn* and using the <cite>hist</cite> splitting
+method discretize training data by creating a histogram with a configurable
+number of bins. The following keyword arguments can be used to influence the
+created histogram.</p>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 17%" />
+<col style="width: 17%" />
+<col style="width: 17%" />
+<col style="width: 50%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Keyword argument</p></th>
+<th class="head"><p>Possible values</p></th>
+<th class="head"><p>Default value</p></th>
+<th class="head"><p>Description</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">maxBins</span></code></p></td>
+<td><p><cite>[0, inf)</cite></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">256</span></code></p></td>
+<td><p>Number of bins in the histogram with the discretized training data. The
+value <code class="docutils literal notranslate"><span class="pre">0</span></code> disables data discretization.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">minBinSize</span></code></p></td>
+<td><p><cite>[1, inf)</cite></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">5</span></code></p></td>
+<td><p>Minimum number of training data points in each bin after discretization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">binningStrategy</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">quantiles,</span> <span class="pre">averages</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">quantiles</span></code></p></td>
+<td><p>Selects the algorithm used to calculate bin edges. <code class="docutils literal notranslate"><span class="pre">quantiles</span></code>
+results in bins with a similar amount of training data points. <code class="docutils literal notranslate"><span class="pre">averages</span></code>
+divides the range of values observed in the training data set into
+equal-width bins of size <cite>(max - min) / maxBins</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+<p>Note that using discretized training data can greatly accelerate model training
+times, especially for larger data sets. However, due to the reduced fidelity of
+the data, the resulting model can present worse performance metrics compared to
+a model trained on the original data. In such cases, the number of bins can be
+increased with the <code class="docutils literal notranslate"><span class="pre">maxBins</span></code> parameter, or binning can be disabled entirely by
+setting <code class="docutils literal notranslate"><span class="pre">maxBins=0</span></code>.</p>
+</section>
+</section>
+
+
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+            Intel(R) Extension for Scikit-learn*
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+      current_version = "2024.7"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
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+  <section id="intelex">
+<span id="index"></span><h1>Intel® Extension for Scikit-learn*<a class="headerlink" href="#intelex" title="Permalink to this heading"></a></h1>
+<p>Intel(R) Extension for Scikit-learn is a <strong>free software AI accelerator</strong> designed to deliver up to <strong>100X</strong> faster performance for your existing scikit-learn code.
+The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.</p>
+<p class="rubric">Designed for Data Scientists and Framework Designers</p>
+<p>Use Intel(R) Extension for Scikit-learn, to:</p>
+<ul class="simple">
+<li><p>Speed up training and inference by up to 100x with the equivalent mathematical accuracy</p></li>
+<li><p>Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs</p></li>
+<li><p>Integrate the extension into your existing Scikit-learn applications without code modifications</p></li>
+<li><p>Enable and disable the extension with a couple of lines of code or at the command line</p></li>
+</ul>
+<p>Intel(R) Extension for Scikit-learn is also a part of <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html">Intel(R) AI Tools</a>.</p>
+<a class="reference internal image-reference" href="_images/scikit-learn-acceleration.PNG"><img alt="_images/scikit-learn-acceleration.PNG" src="_images/scikit-learn-acceleration.PNG" style="width: 800px;" /></a>
+<p>These performance charts use benchmarks that you can find in the <a class="reference external" href="https://github.com/IntelPython/scikit-learn_bench">scikit-learn bench repository</a>.</p>
+<section id="supported-algorithms">
+<h2>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Permalink to this heading"></a></h2>
+<p>See all of the <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a>.</p>
+</section>
+<section id="intel-r-optimizations">
+<h2>Intel(R) Optimizations<a class="headerlink" href="#intel-r-optimizations" title="Permalink to this heading"></a></h2>
+<section id="enable-intel-r-cpu-optimizations">
+<h3>Enable Intel(R) CPU Optimizations<a class="headerlink" href="#enable-intel-r-cpu-optimizations" title="Permalink to this heading"></a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="enable-intel-r-gpu-optimizations">
+<h3>Enable Intel(R) GPU optimizations<a class="headerlink" href="#enable-intel-r-gpu-optimizations" title="Permalink to this heading"></a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">dpctl</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span><span class="p">,</span> <span class="n">config_context</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">target_offload</span><span class="o">=</span><span class="s2">&quot;gpu:0&quot;</span><span class="p">):</span>
+    <span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
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+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Kaggle Kernels</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
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+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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+  <section id="kaggle-kernels">
+<span id="kaggle"></span><h1>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Permalink to this heading"></a></h1>
+<p>See Kaggle notebooks created by Intel® Extension for Scikit-learn* developers.</p>
+<section id="acceleration">
+<h2>Acceleration<a class="headerlink" href="#acceleration" title="Permalink to this heading"></a></h2>
+<p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook">Introduction to scikit-learn-intelex</a>
+provides a summary of the speedup you can achieve with Intel® Extension for Scikit-learn*.</p>
+</section>
+<section id="machine-learning-workflows">
+<h2>Machine Learning Workflows<a class="headerlink" href="#machine-learning-workflows" title="Permalink to this heading"></a></h2>
+<p>Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.</p>
+<section id="kaggle-kernels-that-use-scikit-learn-and-intelex">
+<h3>Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-scikit-learn-and-intelex" title="Permalink to this heading"></a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="kaggle-kernels-that-use-automl-with-intelex">
+<h3>Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-automl-with-intelex" title="Permalink to this heading"></a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</div>
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+</section>
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+            Intel(R) Extension for Scikit-learn*
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../kaggle.html">Kaggle Kernels</a></li>
+      <li class="breadcrumb-item active">Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/kaggle/automl.rst.txt" rel="nofollow"> View page source</a>
+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="kaggle-kernels-that-use-automl-and-intelex">
+<h1>Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*<a class="headerlink" href="#kaggle-kernels-that-use-automl-and-intelex" title="Permalink to this heading"></a></h1>
+<p>The following Kaggle kernels show how to patch autoML frameworks with Intel® Extension for Scikit-learn*.</p>
+<p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 67%" />
+<col style="width: 33%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alex97andreev/tps-jun-autogluon-with-sklearnex">AutoML MultiClass Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Jun 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/lordozvlad/titanic-automl-with-intel-extension-for-sklearn/notebook">AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> Titanic datset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/lordozvlad/fast-automl-with-intel-extension-for-scikit-learn/notebook">AutoML Binary Classification (Gradient Boosting, Random Forest) using AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Oct 2021] Synthetic molecular response data</p>
+</td>
+<td><p>Predict the biological response of molecules given various chemical properties</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/lordozvlad/tps-nov-automl-with-intel-extension">AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using EvalML and AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Nov 2021] Synthetic spam emails data</p>
+</td>
+<td><p>Identify spam emails via features extracted from the email</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex/notebook">AutoML Binary Classification (Random Forest, SVR, Blending) using PyCaret with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Jan 2022] Fictional Sales data</p>
+</td>
+<td><p>Predict the corresponding item sales for each date-country-store-item combination</p></td>
+</tr>
+</tbody>
+</table>
+</section>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
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+  <section id="kaggle-kernels-for-classification-tasks">
+<h1>Kaggle Kernels for Classification Tasks<a class="headerlink" href="#kaggle-kernels-for-classification-tasks" title="Permalink to this heading"></a></h1>
+<p>The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various classification tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.</p>
+<p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
+<section id="binary-classification">
+<h2>Binary Classification<a class="headerlink" href="#binary-classification" title="Permalink to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 38%" />
+<col style="width: 25%" />
+<col style="width: 38%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-nov-log-regression-with-sklearnex-17x-speedup">Logistic Regression for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Nov 2021] Synthetic spam emails data</p>
+</td>
+<td><p>Identify spam emails via features extracted from the email</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing (normalization)</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/fast-feature-importance-using-scikit-learn-intelex/notebook">Feature Importance in Random Forest for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Nov 2021] Synthetic spam emails data</p>
+</td>
+<td><p>Identify spam emails via features extracted from the email</p></td>
+<td><ul class="simple">
+<li><p>reducing DataFrame memory usage</p></li>
+<li><p>computing feature importance with ELI5 and the default scikit-learn permutation importance</p></li>
+<li><p>training using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/andreyrus/tps-apr-rf-with-intel-extension-for-scikit-learn">Random Forest for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Apr 2021] Synthetic data based on Titanic dataset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature construction</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/tps04-svm-with-intel-extension-for-scikit-learn">Support Vector Classification (SVC) for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Apr 2021] Synthetic data based on Titanic dataset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/tps04-svm-with-scikit-learn-intelex">Support Vector Classification (SVC) with Feature Preprocessing for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Apr 2021] Synthetic data based on Titanic dataset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature engineering</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="multiclass-classification">
+<h2>MultiClass Classification<a class="headerlink" href="#multiclass-classification" title="Permalink to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 38%" />
+<col style="width: 25%" />
+<col style="width: 38%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/tps-jun-fast-logreg-with-scikit-learn-intelex">Logistic Regression for MultiClass Classification with Quantile Transformer</a></p>
+<p><strong>Data:</strong> [TPS Jun 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing with Quantile Transformer</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex">Support Vector Classification (SVC) for MultiClass Classification</a></p>
+<p><strong>Data:</strong> [TPS May 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/owerbat/tps-jun-fast-stacking-with-scikit-learn-intelex">Stacking Classifer with Logistic Regression, kNN, Random Forest, and Quantile Transformer</a></p>
+<p><strong>Data:</strong> [TPS Jun 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing: one-hot encoding, dimensionality reduction with PCA, normalization</p></li>
+<li><p>creating a stacking classifier with logistic regression, kNN, and random forest,
+and a pipeline of Quantile Transformer and another logistic regression as a final estimator</p></li>
+<li><p>searching for optimal parameters for the stacking classifier</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup">Support Vector Classification (SVC) for MultiClass Classification</a></p>
+<p><strong>Data:</strong> [TPS Dec 2021] Synthetic Forest Cover Type data</p>
+</td>
+<td><p>Predict the forest cover type</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/tps-dec-fast-feature-importance-with-sklearnex">Feature Importance in Random Forest for MultiClass Classification</a></p>
+<p><strong>Data:</strong> [TPS Dec 2021] Synthetic Forest Cover Type data</p>
+</td>
+<td><p>Predict the forest cover type</p></td>
+<td><ul class="simple">
+<li><p>reducing DataFrame memory usage</p></li>
+<li><p>computing feature importance with ELI5</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/alexeykolobyanin/tps-feb-knn-with-sklearnex-13x-speedup">k-Nearest Neighbors (kNN) for MultiClass Classification</a></p>
+<p><strong>Data:</strong> [TPS Feb 2022] Bacteria DNA</p>
+</td>
+<td><p>Predict bacteria species based on repeated lossy measurements of DNA snippets</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="classification-tasks-in-computer-vision">
+<h2>Classification Tasks in Computer Vision<a class="headerlink" href="#classification-tasks-in-computer-vision" title="Permalink to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 38%" />
+<col style="width: 25%" />
+<col style="width: 38%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-mnist?scriptVersionId=58739300">Support Vector Classification (SVC) for MultiClass Classification (CV task)</a></p>
+<p><strong>Data:</strong> Digit Recognizer (MNIST)</p>
+</td>
+<td><p>Recognize hand-written digits</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/fast-knn-using-scikit-learn-intelex-for-mnist?scriptVersionId=58738635">k-Nearest Neighbors (kNN) for MultiClass Classification (CV task)</a></p>
+<p><strong>Data:</strong> Digit Recognizer (MNIST)</p>
+</td>
+<td><p>Recognize hand-written digits</p></td>
+<td><ul class="simple">
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="classification-tasks-in-natural-language-processing">
+<h2>Classification Tasks in Natural Language Processing<a class="headerlink" href="#classification-tasks-in-natural-language-processing" title="Permalink to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 38%" />
+<col style="width: 25%" />
+<col style="width: 38%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-nlp?scriptVersionId=58739339">Support Vector Classification (SVC) for a Binary Classification (NLP task)</a></p>
+<p><strong>Data:</strong> Natural Language Processing with Disaster Tweets</p>
+</td>
+<td><p>Predict which tweets are about real disasters and which ones are not</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>TF-IDF calculation</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/using-scikit-learn-intelex-for-what-s-cooking?scriptVersionId=58739642">One-vs-Rest Support Vector Machine (SVM) with Text Data for MultiClass Classification</a></p>
+<p><strong>Data:</strong> What’s Cooking</p>
+</td>
+<td><p>Use recipe ingredients to predict the cuisine</p></td>
+<td><ul class="simple">
+<li><p>feature extraction using TfidfVectorizer</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/code/alex97andreev/fast-svm-for-sparse-data-from-nlp-problem">Support Vector Classification (SVC) for Binary Classification with Sparse Data (NLP task)</a></p>
+<p><strong>Data:</strong> Stack Overflow questions</p>
+</td>
+<td><p>Predict the binary quality rating for Stack Overflow questions</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>TF-IDF calculation</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
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+  <section id="kaggle-kernels-for-regression-tasks">
+<h1>Kaggle Kernels for Regression Tasks<a class="headerlink" href="#kaggle-kernels-for-regression-tasks" title="Permalink to this heading"></a></h1>
+<p>The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various regression tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.</p>
+<p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
+<section id="using-a-single-regressor">
+<h2>Using a Single Regressor<a class="headerlink" href="#using-a-single-regressor" title="Permalink to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 38%" />
+<col style="width: 25%" />
+<col style="width: 38%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/tps-jul-2021-baseline-with-nusvr-model/notebook">Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-aug-nusvr-with-intel-extension-for-sklearn">Nu Support Vector Regression (nuSVR)</a></p>
+<p><strong>Data:</strong> [TPS Aug 2021] Synthetic loan data</p>
+</td>
+<td><p>Calculate loss associated with a loan defaults</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature engineering</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/house-prices-nusvr-sklearn-intelex-4x-speedup">Nu Support Vector Regression (nuSVR)</a></p>
+<p><strong>Data:</strong> House Prices dataset</p>
+</td>
+<td><p>Predict sale prices for a property based on its characteristics</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>exploring outliers</p></li>
+<li><p>feature engineering</p></li>
+<li><p>filling missing values</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/pahandrovich/tps-jul-2021-fast-randomforest-with-sklearnex">Random Forest Regression</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>checking correlation between features</p></li>
+<li><p>search for best paramters using GridSearchCV</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-jul-rf-with-intel-extension-for-scikit-learn">Random Forest Regression with Feature Engineering</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature engineering</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/tps-mar-fast-workflow-using-scikit-learn-intelex">Random Forest Regression with Feature Importance Computation</a></p>
+<p><strong>Data:</strong> [TPS Mar 2022] Spatio-temporal traffic data</p>
+</td>
+<td><p>Forecast twelve-hours of traffic flow in a major U.S. metropolitan area</p></td>
+<td><ul class="simple">
+<li><p>feature engineering</p></li>
+<li><p>computing feature importance with ELI5</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-sep-ridge-with-sklearn-intelex-2x-speedup">Ridge Regression</a></p>
+<p><strong>Data:</strong> [TPS Sep 2021] Synthetic insurance data</p>
+</td>
+<td><p>Predict the probability of a customer making a claim upon an insurance policy</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>filling missing values</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="stacking-regressors">
+<h2>Stacking Regressors<a class="headerlink" href="#stacking-regressors" title="Permalink to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 38%" />
+<col style="width: 25%" />
+<col style="width: 38%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-jul-stacking-with-scikit-learn-intelex">Stacking Regressor with Random Fores, SVR, and LASSO</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>feature engineering</p></li>
+<li><p>creating a stacking regressor</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/predict-sales-stacking-with-scikit-learn-intelex">Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data</a></p>
+<p><strong>Data:</strong> Predict Future Sales dataset</p>
+</td>
+<td><p>Predict total sales for every product and store in the next month based on daily sales data</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>creating a stacking regressor</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
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+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
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+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
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+      <li class="breadcrumb-item active">oneAPI and GPU support in Intel® Extension for Scikit-learn*</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/oneapi-gpu.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="oneapi-and-gpu-support-in-intelex">
+<span id="oneapi-gpu"></span><h1>oneAPI and GPU support in Intel® Extension for Scikit-learn*<a class="headerlink" href="#oneapi-and-gpu-support-in-intelex" title="Permalink to this heading"></a></h1>
+<p>Intel® Extension for Scikit-learn* supports oneAPI concepts, which
+means that algorithms can be executed on different devices: CPUs and GPUs.
+This is done via integration with
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> package that
+implements core oneAPI concepts like queues and devices.</p>
+<section id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this heading"></a></h2>
+<p>For execution on GPU, DPC++ compiler runtime and driver are required. Refer to <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html">DPC++ system
+requirements</a> for details.</p>
+<p>DPC++ compiler runtime can be installed either from PyPI or Anaconda:</p>
+<ul>
+<li><p>Install from PyPI:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">dpcpp</span><span class="o">-</span><span class="n">cpp</span><span class="o">-</span><span class="n">rt</span>
+</pre></div>
+</div>
+</li>
+<li><p>Install using Conda via the Intel repository:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">dpcpp_cpp_rt</span> <span class="o">-</span><span class="n">c</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">software</span><span class="o">.</span><span class="n">repos</span><span class="o">.</span><span class="n">intel</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">python</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span>
+</pre></div>
+</div>
+</li>
+</ul>
+</section>
+<section id="device-offloading">
+<h2>Device offloading<a class="headerlink" href="#device-offloading" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn* offers two options for running an algorithm on a
+specific device with the help of dpctl:</p>
+<ul>
+<li><p>Pass input data as <a class="reference external" href="https://intelpython.github.io/dpctl/latest/docfiles/dpctl/usm_ndarray.html#dpctl.tensor.usm_ndarray">dpctl.tensor.usm_ndarray</a> to the algorithm.</p>
+<p>The computation will run on the device where the input data is
+located, and the result will be returned as <code class="code docutils literal notranslate"><span class="pre">usm_ndarray</span></code> to the same
+device.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>All the input data for an algorithm must reside on the same device.</p>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The <code class="code docutils literal notranslate"><span class="pre">usm_ndarray</span></code> can only be consumed by the base methods
+like <code class="code docutils literal notranslate"><span class="pre">fit</span></code>, <code class="code docutils literal notranslate"><span class="pre">predict</span></code>, and <code class="code docutils literal notranslate"><span class="pre">transform</span></code>.
+Note that only the algorithms in Intel® Extension for Scikit-learn* support
+<code class="code docutils literal notranslate"><span class="pre">usm_ndarray</span></code>. The algorithms from the stock version of scikit-learn
+do not support this feature.</p>
+</div>
+</li>
+<li><p>Use global configurations of Intel® Extension for Scikit-learn**:</p>
+<ol class="arabic simple">
+<li><p>The <code class="code docutils literal notranslate"><span class="pre">target_offload</span></code> option can be used to set the device primarily
+used to perform computations. Accepted data types are <code class="code docutils literal notranslate"><span class="pre">str</span></code> and
+<code class="code docutils literal notranslate"><span class="pre">dpctl.SyclQueue</span></code>. If you pass a string to <code class="code docutils literal notranslate"><span class="pre">target_offload</span></code>,
+it should either be <code class="docutils literal notranslate"><span class="pre">&quot;auto&quot;</span></code>, which means that the execution
+context is deduced from the location of input data, or a string
+with SYCL* filter selector. The default value is <code class="docutils literal notranslate"><span class="pre">&quot;auto&quot;</span></code>.</p></li>
+<li><p>The <code class="code docutils literal notranslate"><span class="pre">allow_fallback_to_host</span></code> option
+is a Boolean flag. If set to <code class="code docutils literal notranslate"><span class="pre">True</span></code>, the computation is allowed
+to fallback to the host device when a particular estimator does not support
+the selected device. The default value is <code class="code docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+</ol>
+</li>
+</ul>
+<p>These options can be set using <code class="code docutils literal notranslate"><span class="pre">sklearnex.set_config()</span></code> function or
+<code class="code docutils literal notranslate"><span class="pre">sklearnex.config_context</span></code>. To obtain the current values of these options,
+call <code class="code docutils literal notranslate"><span class="pre">sklearnex.get_config()</span></code>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Functions <code class="code docutils literal notranslate"><span class="pre">set_config</span></code>, <code class="code docutils literal notranslate"><span class="pre">get_config</span></code> and <code class="code docutils literal notranslate"><span class="pre">config_context</span></code>
+are always patched after the <code class="code docutils literal notranslate"><span class="pre">sklearnex.patch_sklearn()</span></code> call.</p>
+</div>
+<p class="rubric">Compatibility considerations</p>
+<p>For compatibility reasons, algorithms in Intel® Extension for Scikit-learn* may be offloaded to the device using
+<code class="code docutils literal notranslate"><span class="pre">daal4py.oneapi.sycl_context</span></code>. However, it is recommended to use one of the options
+described above for device offloading instead of using <code class="code docutils literal notranslate"><span class="pre">sycl_context</span></code>.</p>
+</section>
+<section id="example">
+<h2>Example<a class="headerlink" href="#example" title="Permalink to this heading"></a></h2>
+<p>An example on how to patch your code with Intel CPU/GPU optimizations:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span><span class="p">,</span> <span class="n">config_context</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">target_offload</span><span class="o">=</span><span class="s2">&quot;gpu:0&quot;</span><span class="p">):</span>
+   <span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Current offloading behavior restricts fitting and inference of any models to be
+in the same context or absence of context. For example, a model trained in the GPU context with
+target_offload=”gpu:0” throws an error if the inference is made outside the same GPU context.</p>
+</div>
+</section>
+</section>
+
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Quick Start</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/quick-start.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="quick-start">
+<h1>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this heading"></a></h1>
+<p>Get ready to elevate your scikit-learn code with Intel® Extension for Scikit-learn* and experience the benefits of accelerated performance in just a few simple steps.</p>
+<section id="compatibility-with-scikit-learn">
+<h2>Compatibility with Scikit-learn*<a class="headerlink" href="#compatibility-with-scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.</p>
+</section>
+<section id="integrate-intelex">
+<h2>Integrate Intel® Extension for Scikit-learn*<a class="headerlink" href="#integrate-intelex" title="Permalink to this heading"></a></h2>
+<section id="patching">
+<h3>Patching<a class="headerlink" href="#patching" title="Permalink to this heading"></a></h3>
+<p>Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension.
+This action is called <code class="docutils literal notranslate"><span class="pre">patching</span></code>. This is not a permanent change so you can always undo the patching if necessary.</p>
+<p>To patch Intel® Extension for Scikit-learn, use one of these methods:</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Method</p></th>
+<th class="head"><p>Action</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Use a flag in the command line</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span> <span class="n">my_application</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+</td>
+</tr>
+<tr class="row-odd"><td><p>Modify your script</p></td>
+<td><p>Add the following lines:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</td>
+</tr>
+<tr class="row-even"><td><p>Import an estimator from the <code class="docutils literal notranslate"><span class="pre">sklearnex</span></code> module</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
+</pre></div>
+</div>
+</td>
+</tr>
+</tbody>
+</table>
+<p>These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.</p>
+<p><strong>Example</strong></p>
+<p>This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.</p>
+<div class="literal-block-wrapper docutils container" id="id2">
+<div class="code-block-caption"><span class="caption-text">Example: Drop-In Patching</span><a class="headerlink" href="#id2" title="Permalink to this code"></a></div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>  <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+  <span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+  <span class="n">patch_sklearn</span><span class="p">()</span>
+
+  <span class="c1"># You need to re-import scikit-learn algorithms after the patch</span>
+  <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+  <span class="c1"># The use of the original Scikit-learn is not changed</span>
+  <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">0</span><span class="p">],</span>
+              <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
+  <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+  <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;kmeans.labels_ = </span><span class="si">{</span><span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="global-patching">
+<h3>Global Patching<a class="headerlink" href="#global-patching" title="Permalink to this heading"></a></h3>
+<p>You can also use global patching to patch all your scikit-learn applications without any additional actions.</p>
+<p>Before you begin, make sure that you have read and write permissions for Scikit-learn files.</p>
+<p>With global patching, you can:</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Task</p></th>
+<th class="head"><p>Action</p></th>
+<th class="head"><p>Note</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Patch all supported algorithms</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
+</pre></div>
+</div>
+</td>
+<td><p>If you run the global patching command several times with different parameters, then only the last configuration is applied.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Patch selected algorithms</p></td>
+<td><p>Use <code class="docutils literal notranslate"><span class="pre">--algorithm</span></code> or <code class="docutils literal notranslate"><span class="pre">-a</span></code> keys with a list of algorithms to patch. For example, to patch only <code class="docutils literal notranslate"><span class="pre">SVC</span></code> and <code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> estimators, run</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>Enable global patching via code</p></td>
+<td><p>Use the <code class="docutils literal notranslate"><span class="pre">patch_sklearn</span></code> function with the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">sklearn</span>
+</pre></div>
+</div>
+</td>
+<td><p>After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Disable patching notifications</p></td>
+<td><p>Use <code class="docutils literal notranslate"><span class="pre">--no-verbose</span></code> or <code class="docutils literal notranslate"><span class="pre">-nv</span></code> keys:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span> <span class="o">-</span><span class="n">nv</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>Disable global patching</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">unpatch_sklearn</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p>Disable global patching via code</p></td>
+<td><p>Use the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument in the <code class="docutils literal notranslate"><span class="pre">unpatch_sklearn</span></code> function</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+<span class="n">unpatch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>If you clone an environment with enabled global patching, it will already be applied in the new environment.</p>
+</div>
+</section>
+<section id="unpatching">
+<h3>Unpatching<a class="headerlink" href="#unpatching" title="Permalink to this heading"></a></h3>
+<p>To undo the patch (also called <cite>unpatching</cite>) is to return scikit-learn to original implementation and
+replace patched algorithms with the stock scikit-learn algorithms.</p>
+<p>To unpatch successfully, you must reimport the scikit-learn package:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sklearnex</span><span class="o">.</span><span class="n">unpatch_sklearn</span><span class="p">()</span>
+<span class="c1"># Re-import scikit-learn algorithms after the unpatch</span>
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h2>
+<nav class="contents local" id="contents">
+<ul class="simple">
+<li><p><a class="reference internal" href="#install-from-pypi" id="id6">Install from PyPI</a></p></li>
+<li><p><a class="reference internal" href="#install-from-anaconda-cloud" id="id7">Install from Anaconda* Cloud</a></p></li>
+<li><p><a class="reference internal" href="#build-from-sources" id="id8">Build from Sources</a></p></li>
+<li><p><a class="reference internal" href="#install-intel-r-ai-tools" id="id9">Install Intel*(R) AI Tools</a></p></li>
+</ul>
+</nav>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>To prevent version conflicts, we recommend creating and activating a new environment for Intel® Extension for Scikit-learn*.</p>
+</div>
+<section id="install-from-pypi">
+<h3><a class="toc-backref" href="#id6" role="doc-backlink">Install from PyPI</a><a class="headerlink" href="#install-from-pypi" title="Permalink to this heading"></a></h3>
+<p>Recommended by default.</p>
+<p>To install Intel® Extension for Scikit-learn*, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
+</pre></div>
+</div>
+<p><strong>Supported Configurations</strong></p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="install-from-anaconda-cloud">
+<h3><a class="toc-backref" href="#id7" role="doc-backlink">Install from Anaconda* Cloud</a><a class="headerlink" href="#install-from-anaconda-cloud" title="Permalink to this heading"></a></h3>
+<p>To prevent version conflicts, we recommend installing <cite>scikit-learn-intelex</cite> into a new conda environment.</p>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-0-0-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-0-0-0" name="0-0" role="tab" tabindex="0">Conda-Forge channel</button><button aria-controls="panel-0-0-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-1" name="0-1" role="tab" tabindex="-1">Intel channel</button><button aria-controls="panel-0-0-2" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-2" name="0-2" role="tab" tabindex="-1">Main channel</button></div><div aria-labelledby="tab-0-0-0" class="sphinx-tabs-panel" id="panel-0-0-0" name="0-0" role="tabpanel" tabindex="0"><p>Recommended by default.</p>
+<p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id3">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id3" title="Permalink to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div><div aria-labelledby="tab-0-0-1" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-1" name="0-1" role="tabpanel" tabindex="0"><p>Recommended for the Intel® Distribution for Python users.</p>
+<p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">software</span><span class="o">.</span><span class="n">repos</span><span class="o">.</span><span class="n">intel</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">python</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id4">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id4" title="Permalink to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div><div aria-labelledby="tab-0-0-2" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-2" name="0-2" role="tabpanel" tabindex="0"><p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id5">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id5" title="Permalink to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div></div>
+</section>
+<section id="build-from-sources">
+<h3><a class="toc-backref" href="#id8" role="doc-backlink">Build from Sources</a><a class="headerlink" href="#build-from-sources" title="Permalink to this heading"></a></h3>
+<p>See <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md">Installation instructions</a> to build Intel® Extension for Scikit-learn* from the sources.</p>
+</section>
+<section id="install-intel-r-ai-tools">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Install Intel*(R) AI Tools</a><a class="headerlink" href="#install-intel-r-ai-tools" title="Permalink to this heading"></a></h3>
+<p>Download the Intel AI Tools <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html">here</a>. The extension is already included.</p>
+</section>
+</section>
+<section id="release-notes">
+<h2>Release Notes<a class="headerlink" href="#release-notes" title="Permalink to this heading"></a></h2>
+<p>See the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/releases">Release Notes</a> for each version of Intel® Extension for Scikit-learn*.</p>
+</section>
+<section id="system-requirements">
+<h2>System Requirements<a class="headerlink" href="#system-requirements" title="Permalink to this heading"></a></h2>
+<section id="hardware-requirements">
+<h3>Hardware Requirements<a class="headerlink" href="#hardware-requirements" title="Permalink to this heading"></a></h3>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-1-1-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-1-1-0" name="1-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-1-1-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-1-1-1" name="1-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-1-1-0" class="sphinx-tabs-panel" id="panel-1-1-0" name="1-0" role="tabpanel" tabindex="0"><p>All processors with <code class="docutils literal notranslate"><span class="pre">x86</span></code> architecture with at least one of the following instruction sets:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>SSE2</p></li>
+<li><p>SSE4.2</p></li>
+<li><p>AVX2</p></li>
+<li><p>AVX512</p></li>
+</ul>
+</div></blockquote>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>ARM* architecture is not supported.</p>
+</div>
+</div><div aria-labelledby="tab-1-1-1" class="sphinx-tabs-panel" hidden="true" id="panel-1-1-1" name="1-1" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>All Intel® integrated and discrete GPUs</p></li>
+<li><p>Intel® GPU drivers</p></li>
+</ul>
+</div></div>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our <a class="reference internal" href="blogs.html#blogs"><span class="std std-ref">blogs</span></a>.</p>
+</div>
+</section>
+<section id="software-requirements">
+<h3>Software Requirements<a class="headerlink" href="#software-requirements" title="Permalink to this heading"></a></h3>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-2-2-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-2-2-0" name="2-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-2-2-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-2-2-1" name="2-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-2-2-0" class="sphinx-tabs-panel" id="panel-2-2-0" name="2-0" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
+<li><p>Windows* OS 10 or newer</p></li>
+<li><p>Windows* Server 2019 or newer</p></li>
+</ul>
+</div><div aria-labelledby="tab-2-2-1" class="sphinx-tabs-panel" hidden="true" id="panel-2-2-1" name="2-1" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
+<li><p>Windows* OS 10 or newer</p></li>
+<li><p>Windows* Server 2019 or newer</p></li>
+</ul>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<p>If you use accelerators, refer to <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html">oneAPI DPC++/C++ Compiler System Requirements</a>.</p>
+</div>
+</div></div>
+<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:</p>
+<ul class="simple">
+<li><p>1.0.X</p></li>
+<li><p>1.1.X</p></li>
+<li><p>1.2.X</p></li>
+<li><p>1.3.X</p></li>
+</ul>
+</section>
+<section id="memory-requirements">
+<h3>Memory Requirements<a class="headerlink" href="#memory-requirements" title="Permalink to this heading"></a></h3>
+<p>By default, algorithms in Intel® Extension for Scikit-learn* run in the multi-thread mode. This mode uses all available threads.
+Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Single-thread mode</p></th>
+<th class="head"><p>Multi-thread mode</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>SVM</p></td>
+<td><p>Both Scikit-learn and Intel® Extension for Scikit-learn* consume approximately the same amount of RAM.</p></td>
+<td><p>In Intel® Extension for Scikit-learn*, an algorithm with <code class="docutils literal notranslate"><span class="pre">N</span></code> threads consumes <code class="docutils literal notranslate"><span class="pre">N</span></code> times more RAM.</p></td>
+</tr>
+</tbody>
+</table>
+<p>In all Intel® Extension for Scikit-learn* algorithms with GPU support, computations run on device memory.
+The device memory must be large enough to store a copy of the entire dataset.
+You may also require additional device memory for internal arrays that are used in computation.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="samples.html#samples"><span class="std std-ref">Samples</span></a></p>
+</div>
+</section>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+<jinja2.runtime.BlockReference object at 0x7efe6c6bb210>
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+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
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+    <div id="version-switcher-button"
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+      Choose version  <!-- this text may get changed later by javascript -->
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new file mode 100644
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+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Samples &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
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+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="samples/knn_mnist.html" />
+    <link rel="prev" title="Quick Start" href="quick-start.html" />
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+  <div class="version-switcher__container dropdown">
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
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+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
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+            <a href="_sources/samples.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="samples">
+<span id="id1"></span><h1>Samples<a class="headerlink" href="#samples" title="Permalink to this heading"></a></h1>
+<p>The following samples are also provided as Jupyter notebooks in Intel® Extension for Scikit-learn* repository.
+If you want to run them locally, refer to <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks">these instructions</a>.</p>
+<p class="rubric">Classification Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+</ul>
+</div>
+<p class="rubric">Regression Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+</ul>
+</div>
+<p class="rubric">Clustering Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+</ul>
+</div>
+<p class="rubric">Jupyter Notebook Samples</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p>There are also <a class="reference internal" href="kaggle.html#kaggle"><span class="std std-ref">Kaggle kernels</span></a> that use Intel® Extension for Scikit-learn* for a variety of machine learning scenarios.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Explore the complete list of oneAPI code samples in the <a class="reference external" href="https://oneapi-src.github.io/oneAPI-samples/">oneAPI Samples Catalog</a>. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.</p>
+</div>
+</section>
+
+
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
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+</ul>
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+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset</li>
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+            <a href="../_sources/samples/ElasticNet.ipynb.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-ElasticNet-for-Airlines-DepDelay-dataset">
+<h1>Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-ElasticNet-for-Airlines-DepDelay-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Airlines_DepDelay_10M&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to this heading"></a></h2>
+<p>Let’s encode categorical features with LabelEncoder</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;UniqueCarrier&quot;</span><span class="p">,</span> <span class="s2">&quot;Origin&quot;</span><span class="p">,</span> <span class="s2">&quot;Dest&quot;</span><span class="p">]:</span>
+    <span class="n">le</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+    <span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((9000000, 9), (1000000, 9), (9000000,), (1000000,))
+</pre></div></div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_train</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">ElasticNet</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="mf">0.3</span><span class="p">,</span>
+    <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s2">&quot;l1_ratio&quot;</span><span class="p">:</span> <span class="mf">0.7</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">ElasticNet</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.28 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 1.0109113399224974&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class ElasticNet</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">ElasticNet</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">ElasticNet</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 3.96 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the ElasticNet algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 1.0109113399545733&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="svc_adult.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn SVC for Adult dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="lasso_regression.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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diff --git a/2024.7/samples/ElasticNet.ipynb b/2024.7/samples/ElasticNet.ipynb
new file mode 100644
index 0000000000..29e5dca851
--- /dev/null
+++ b/2024.7/samples/ElasticNet.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/daal4py_data_science.html b/2024.7/samples/daal4py_data_science.html
new file mode 100644
index 0000000000..04d08d4cf2
--- /dev/null
+++ b/2024.7/samples/daal4py_data_science.html
@@ -0,0 +1,825 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Utilizing daal4py in Data Science Workflows &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
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+</ul>
+</li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+      <li class="breadcrumb-item active">Utilizing daal4py in Data Science Workflows</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/daal4py_data_science.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Utilizing-daal4py-in-Data-Science-Workflows">
+<h1>Utilizing daal4py in Data Science Workflows<a class="headerlink" href="#Utilizing-daal4py-in-Data-Science-Workflows" title="Permalink to this heading"></a></h1>
+<p>The notebook below has been made to demonstrate daal4py in a data science context. It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user’s Power output in the absence of a power meter.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">glob</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+
+<span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="n">sys</span><span class="o">.</span><span class="n">version</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;3.8.10 (default, May 19 2021, 18:05:58) \n[GCC 7.3.0]&#39;
+</pre></div></div>
+</div>
+<p>This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage. Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">workout_data_dd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/cycling_dataset.csv&quot;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>altitude</th>
+      <th>cadence</th>
+      <th>distance</th>
+      <th>hr</th>
+      <th>latitude</th>
+      <th>longitude</th>
+      <th>power</th>
+      <th>speed</th>
+      <th>time</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>185.800003</td>
+      <td>51</td>
+      <td>3.46</td>
+      <td>81</td>
+      <td>30.313309</td>
+      <td>-97.732711</td>
+      <td>45</td>
+      <td>3.459</td>
+      <td>2016-10-20T22:01:26.000Z</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>185.800003</td>
+      <td>68</td>
+      <td>7.17</td>
+      <td>82</td>
+      <td>30.313277</td>
+      <td>-97.732715</td>
+      <td>0</td>
+      <td>3.710</td>
+      <td>2016-10-20T22:01:27.000Z</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>186.399994</td>
+      <td>38</td>
+      <td>11.04</td>
+      <td>82</td>
+      <td>30.313243</td>
+      <td>-97.732717</td>
+      <td>42</td>
+      <td>3.874</td>
+      <td>2016-10-20T22:01:28.000Z</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>186.800003</td>
+      <td>38</td>
+      <td>15.18</td>
+      <td>83</td>
+      <td>30.313212</td>
+      <td>-97.732720</td>
+      <td>5</td>
+      <td>4.135</td>
+      <td>2016-10-20T22:01:29.000Z</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>186.600006</td>
+      <td>38</td>
+      <td>19.43</td>
+      <td>83</td>
+      <td>30.313172</td>
+      <td>-97.732723</td>
+      <td>1</td>
+      <td>4.250</td>
+      <td>2016-10-20T22:01:30.000Z</td>
+    </tr>
+  </tbody>
+</table>
+</div></div>
+</div>
+<div class="line-block">
+<div class="line">The data above has several key features that would be of great use here.</div>
+<div class="line">- Altitude can affect performance, so it might be a useful feature.</div>
+<div class="line">- Cadence is the revolutions per minute of the crank, and may have possible influence.</div>
+<div class="line">- Heart Rate is a measure of the body’s workout strain, and would have a high possibly of influence. - Distance may have a loose correlation as it is highly route dependent, but might be possible. - Speed has possible correlations as it ties directly into power.</div>
+</div>
+<section id="Explore-and-visualize-some-of-the-data">
+<h2>Explore and visualize some of the data<a class="headerlink" href="#Explore-and-visualize-some-of-the-data" title="Permalink to this heading"></a></h2>
+<p>In general, we are trying to predict on the ‘power’ in Watts to see if we can generate a model that can predict one’s power output without the usage of a cycling power meter. Below are some basic scatterplots as we explore the data. Scatterplots are great for looking for patterns and correlation in the data itself. Below, we can see that cadence and speed are positively correlated.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;cadence&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;hr&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;cadence&quot;</span><span class="p">,</span> <span class="s2">&quot;speed&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;speed&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;altitude&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;distance&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_0.png" src="../_images/samples_daal4py_data_science_7_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_1.png" src="../_images/samples_daal4py_data_science_7_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_2.png" src="../_images/samples_daal4py_data_science_7_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_3.png" src="../_images/samples_daal4py_data_science_7_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_4.png" src="../_images/samples_daal4py_data_science_7_4.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_5.png" src="../_images/samples_daal4py_data_science_7_5.png" />
+</div>
+</div>
+</section>
+<section id="Using-daal4py-for-Machine-Learning-tasks">
+<h2>Using daal4py for Machine Learning tasks<a class="headerlink" href="#Using-daal4py-for-Machine-Learning-tasks" title="Permalink to this heading"></a></h2>
+<p>In the sections below, we will be using daal4py directly. After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py’s training and prediction classes to generate a workable model.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">daal4py</span> <span class="k">as</span> <span class="nn">d4p</span>
+</pre></div>
+</div>
+</div>
+<p>It is now the time to split the dataset into train and test sets. This is demonstrated below.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">workout_data_dd</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">train_set</span> <span class="o">=</span> <span class="n">workout_data_dd</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3000</span><span class="p">]</span>
+<span class="n">test_set</span> <span class="o">=</span> <span class="n">workout_data_dd</span><span class="p">[</span><span class="mi">3000</span><span class="p">:]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">train_set</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_set</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(3902, 9)
+(3000, 9) (902, 9)
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Reduce the dataset, create X.  We drop the target, and other non-essential features.</span>
+<span class="n">reduced_dataset</span> <span class="o">=</span> <span class="n">train_set</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="c1"># Get the target, create Y</span>
+<span class="n">target</span> <span class="o">=</span> <span class="n">train_set</span><span class="o">.</span><span class="n">power</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="c1"># This is essentially doing np.array(dataset.power.values, ndmin=2).T</span>
+<span class="c1"># as it needs to force a 2 dimensional array as we only have 1 target</span>
+</pre></div>
+</div>
+</div>
+<p>X is 5 features by 3k rows, Y is 3k rows by 1 column</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">reduced_dataset</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(3000, 5) (3000, 1)
+</pre></div></div>
+</div>
+</section>
+<section id="Training-the-model">
+<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Permalink to this heading"></a></h2>
+<p>Create the Linear Regression Model, and train the model with the data. We utilize daal4py’s linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d4p_lm</span> <span class="o">=</span> <span class="n">d4p</span><span class="o">.</span><span class="n">linear_regression_training</span><span class="p">(</span><span class="n">interceptFlag</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">lm_trained</span> <span class="o">=</span> <span class="n">d4p_lm</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">reduced_dataset</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model has this number of features: &quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">NumberOfFeatures</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Model has this number of features:  5
+</pre></div></div>
+</div>
+</section>
+<section id="Prediction-(inference)-with-the-trained-model">
+<h2>Prediction (inference) with the trained model<a class="headerlink" href="#Prediction-(inference)-with-the-trained-model" title="Permalink to this heading"></a></h2>
+<p>Now that the model is trained, we can test it with the test part of the dataset. We drop the same features to match that of the trained model, and put it into daal4py’s linear_regression_prediction class.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">subset</span> <span class="o">=</span> <span class="n">test_set</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Now we can create the Prediction object and use the reduced dataset for prediction. The class’s arguments use the independent data and the trained model from above as the parameters.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lm_predictor_component</span> <span class="o">=</span> <span class="n">d4p</span><span class="o">.</span><span class="n">linear_regression_prediction</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">lm_predictor_component</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">subset</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">prediction</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">300</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">test_set</span><span class="o">.</span><span class="n">power</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">300</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_24_0.png" src="../_images/samples_daal4py_data_science_24_0.png" />
+</div>
+</div>
+<p>The graph above shows the Orange (predicted) result over the Blue (original data). This data is notoriously sparse in features leading to a difficult to predict target!</p>
+</section>
+<section id="Model-properties">
+<h2>Model properties<a class="headerlink" href="#Model-properties" title="Permalink to this heading"></a></h2>
+<p>Another aspect of the model is the trained model’s properties, which are explored below.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Betas:&quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">Beta</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of betas:&quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">NumberOfBetas</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of Features:&quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">NumberOfFeatures</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03
+   8.53155955e-01 -1.09595022e+01]]
+Number of betas: 6
+Number of Features: 5
+</pre></div></div>
+</div>
+</section>
+<section id="Additional-metrics">
+<h2>Additional metrics<a class="headerlink" href="#Additional-metrics" title="Permalink to this heading"></a></h2>
+<p>We can generate metrics on the independent data with daal4py’s low_order_moments() class.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metrics_processor</span> <span class="o">=</span> <span class="n">d4p</span><span class="o">.</span><span class="n">low_order_moments</span><span class="p">()</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">metrics_processor</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">reduced_dataset</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+<span class="n">data</span><span class="o">.</span><span class="n">standardDeviation</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,
+        1.81623064e+00]])
+</pre></div></div>
+</div>
+</section>
+<section id="Migrating-the-trained-model-for-inference-on-external-systems">
+<h2>Migrating the trained model for inference on external systems<a class="headerlink" href="#Migrating-the-trained-model-for-inference-on-external-systems" title="Permalink to this heading"></a></h2>
+<p>Occasionally one may need to migrate the trained model to another system for inference only–this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;trained_model2.pickle&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">model_pi</span><span class="p">:</span>
+    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">model_pi</span><span class="p">)</span>
+    <span class="n">model_pi</span><span class="o">.</span><span class="n">close</span>
+</pre></div>
+</div>
+</div>
+<p>The trained model file above can be moved to an inference-only or embedded system. This is useful if the training is extreamly heavy or computed-limited.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;trained_model2.pickle&quot;</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">model_import</span><span class="p">:</span>
+    <span class="n">lm_import</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">model_import</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>The imported model from file is now usable again. We can check the betas from the model to ensure that the trained model is present.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lm_import</span><span class="o">.</span><span class="n">Beta</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,
+         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])
+</pre></div></div>
+</div>
+</section>
+</section>
+
+
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/2024.7/samples/daal4py_data_science.ipynb b/2024.7/samples/daal4py_data_science.ipynb
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2024.7/samples/daal4py_data_science.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2024.7/samples/dbscan.html b/2024.7/samples/dbscan.html
new file mode 100644
index 0000000000..2cdfb0fd3d
--- /dev/null
+++ b/2024.7/samples/dbscan.html
@@ -0,0 +1,605 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
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+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
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+            <a href="../_sources/samples/dbscan.ipynb.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-DBSCAN-for-spoken-arabic-digit-dataset">
+<h1>Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-DBSCAN-for-spoken-arabic-digit-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">davies_bouldin_score</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;spoken-arabic-digit&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to this heading"></a></h2>
+<p>Split the data into train and test sets</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 6.37 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_opt</span> <span class="o">=</span> <span class="n">davies_bouldin_score</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn Davies-Bouldin score: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class DBSCAN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 469.21 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_original</span> <span class="o">=</span> <span class="n">davies_bouldin_score</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn Davies-Bouldin score: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn Davies-Bouldin score: 0.8542652084275848&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare Davies-Bouldin score of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Davies-Bouldin score of patched Scikit-learn: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Davies-Bouldin score of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">score_opt</span><span class="o">/</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
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+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/2024.7/samples/dbscan.ipynb b/2024.7/samples/dbscan.ipynb
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2024.7/samples/dbscan.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/kmeans.html b/2024.7/samples/kmeans.html
new file mode 100644
index 0000000000..92095ec729
--- /dev/null
+++ b/2024.7/samples/kmeans.html
@@ -0,0 +1,625 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/doctools.js"></script>
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+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset" href="dbscan.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" href="ridge_regression.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+      </script>
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+</head>
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+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
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+          
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+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
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+    <script>
+      current_version = "2024.7"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/kmeans.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Kmeans-for-spoken-arabic-digit-dataset">
+<h1>Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Kmeans-for-spoken-arabic-digit-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;spoken-arabic-digit&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to this heading"></a></h2>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((236930, 14), (26326, 14), (236930,), (26326,))
+</pre></div></div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_clusters&quot;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">123</span><span class="p">,</span>
+    <span class="s2">&quot;copy_x&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 7.36 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inertia_opt</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">inertia_</span>
+<span class="n">n_iter_opt</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_iter_</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn inertia: </span><span class="si">{</span><span class="n">inertia_opt</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn number of iterations: </span><span class="si">{</span><span class="n">n_iter_opt</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Intel® extension for Scikit-learn inertia: 13346.641333761074
+Intel® extension for Scikit-learn number of iterations: 274
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class KMeans</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 192.14 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inertia_original</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">inertia_</span>
+<span class="n">n_iter_original</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_iter_</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn inertia: </span><span class="si">{</span><span class="n">inertia_original</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn number of iterations: </span><span class="si">{</span><span class="n">n_iter_original</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Original Scikit-learn inertia: 13352.813785961785
+Original Scikit-learn number of iterations: 212
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare inertia and number of iterations of patched Scikit-learn and original&lt;/h3&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;strong&gt;Inertia:&lt;/strong&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn: </span><span class="si">{</span><span class="n">inertia_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Unpatched Scikit-learn: </span><span class="si">{</span><span class="n">inertia_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Ratio: </span><span class="si">{</span><span class="n">inertia_opt</span><span class="o">/</span><span class="n">inertia_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;strong&gt;Number of iterations:&lt;/strong&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn: </span><span class="si">{</span><span class="n">n_iter_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Unpatched Scikit-learn: </span><span class="si">{</span><span class="n">n_iter_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Ratio: </span><span class="si">{</span><span class="p">(</span><span class="n">n_iter_opt</span><span class="o">/</span><span class="n">n_iter_original</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> &lt;br&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Number of iterations is bigger but algorithm is much faster and inertia is lower&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="ridge_regression.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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diff --git a/2024.7/samples/kmeans.ipynb b/2024.7/samples/kmeans.ipynb
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2024.7/samples/kmeans.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/knn_mnist.html b/2024.7/samples/knn_mnist.html
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn KNN for MNIST dataset</li>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-KNN-for-MNIST-dataset">
+<h1>Intel® Extension for Scikit-learn KNN for MNIST dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-KNN-for-MNIST-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;mnist_784&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">72</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((56000, 784), (14000, 784), (56000,), (14000,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_neighbors&quot;</span><span class="p">:</span> <span class="mi">40</span><span class="p">,</span> <span class="s2">&quot;weights&quot;</span><span class="p">:</span> <span class="s2">&quot;distance&quot;</span><span class="p">,</span> <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">knn</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">predicted</span> <span class="o">=</span> <span class="n">knn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">time_opt</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">time_opt</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 1.45 s&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for Intel® extension for Scikit-learn KNN:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for Intel® extension for Scikit-learn KNN:
+              precision    recall  f1-score   support
+
+           0       0.97      0.99      0.98      1365
+           1       0.93      0.99      0.96      1637
+           2       0.99      0.94      0.96      1401
+           3       0.96      0.95      0.96      1455
+           4       0.98      0.96      0.97      1380
+           5       0.95      0.95      0.95      1219
+           6       0.96      0.99      0.97      1317
+           7       0.94      0.95      0.95      1420
+           8       0.99      0.90      0.94      1379
+           9       0.92      0.94      0.93      1427
+
+    accuracy                           0.96     14000
+   macro avg       0.96      0.96      0.96     14000
+weighted avg       0.96      0.96      0.96     14000
+
+
+</pre></div></div>
+</div>
+<p><em>The first column of the classification report above is the class labels.</em></p>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class KNeighborsClassifier.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
+
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">knn</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">predicted</span> <span class="o">=</span> <span class="n">knn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">time_original</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">time_original</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 36.15 s&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for original Scikit-learn KNN:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for original Scikit-learn KNN:
+              precision    recall  f1-score   support
+
+           0       0.97      0.99      0.98      1365
+           1       0.93      0.99      0.96      1637
+           2       0.99      0.94      0.96      1401
+           3       0.96      0.95      0.96      1455
+           4       0.98      0.96      0.97      1380
+           5       0.95      0.95      0.95      1219
+           6       0.96      0.99      0.97      1317
+           7       0.94      0.95      0.95      1420
+           8       0.99      0.90      0.94      1379
+           9       0.92      0.94      0.93      1427
+
+    accuracy                           0.96     14000
+   macro avg       0.96      0.96      0.96     14000
+weighted avg       0.96      0.96      0.96     14000
+
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h2&gt;With scikit-learn-intelex patching you can:&lt;/h2&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">time_original</span><span class="o">/</span><span class="n">time_opt</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
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+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/2024.7/samples/knn_mnist.ipynb b/2024.7/samples/knn_mnist.ipynb
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2024.7/samples/knn_mnist.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/lasso_regression.html b/2024.7/samples/lasso_regression.html
new file mode 100644
index 0000000000..89c3be6eb9
--- /dev/null
+++ b/2024.7/samples/lasso_regression.html
@@ -0,0 +1,642 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" href="linear_regression.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" href="ElasticNet.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
+          (function () {
+            var url = 'https://www.intel.com/content/dam/www/global/wap/tms-loader.js'; // WAP file URL
+            var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = url;
+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+      </script>
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+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset</li>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-Lasso-Regression-for-YearPredictionMSD-dataset">
+<h1>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Lasso-Regression-for-YearPredictionMSD-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset_dir</span> <span class="o">=</span> <span class="s2">&quot;data&quot;</span>
+<span class="n">dataset_name</span> <span class="o">=</span> <span class="s2">&quot;year_prediction_msd&quot;</span>
+<span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip&quot;</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">local_url</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">url</span><span class="p">))</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">local_url</span><span class="p">):</span>
+    <span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="s2">&quot;wb+&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">response</span><span class="o">.</span><span class="n">iter_content</span><span class="p">(</span><span class="mi">8192</span><span class="p">):</span>
+            <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="n">year</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((463810, 90), (51535, 90), (463810,), (51535,))
+</pre></div></div>
+</div>
+</section>
+<section id="Normalize-the-data">
+<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
+    <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Lasso</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.06 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 0.9676607251167297&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class Lasso</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Lasso</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 0.83 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Lasso algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 0.9676599502563477&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="ElasticNet.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
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+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/2024.7/samples/lasso_regression.ipynb b/2024.7/samples/lasso_regression.ipynb
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2024.7/samples/lasso_regression.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/linear_regression.html b/2024.7/samples/linear_regression.html
new file mode 100644
index 0000000000..9002f6b5da
--- /dev/null
+++ b/2024.7/samples/linear_regression.html
@@ -0,0 +1,637 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
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+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/doctools.js"></script>
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+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset" href="nusvr_medical_charges.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset" href="lasso_regression.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
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+            var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = url;
+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
+          })();
+      }
+      </script>
+
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
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+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
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+
+        </div>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/linear_regression.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Linear-Regression-for-YearPredictionMSD-dataset">
+<h1>Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Linear-Regression-for-YearPredictionMSD-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset_dir</span> <span class="o">=</span> <span class="s2">&quot;data&quot;</span>
+<span class="n">dataset_name</span> <span class="o">=</span> <span class="s2">&quot;year_prediction_msd&quot;</span>
+<span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip&quot;</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">local_url</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">url</span><span class="p">))</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">local_url</span><span class="p">):</span>
+    <span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="s2">&quot;wb+&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">response</span><span class="o">.</span><span class="n">iter_content</span><span class="p">(</span><span class="mi">8192</span><span class="p">):</span>
+            <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="n">year</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((463810, 90), (51535, 90), (463810,), (51535,))
+</pre></div></div>
+</div>
+</section>
+<section id="Normalize-the-data">
+<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.03 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 0.7716818451881409&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class LinearRegression</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 0.53 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Linear Regression algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 0.7716856598854065&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="lasso_regression.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+   
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+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
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+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/2024.7/samples/linear_regression.ipynb b/2024.7/samples/linear_regression.ipynb
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2024.7/samples/linear_regression.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/logistictic_regression_cifar.html b/2024.7/samples/logistictic_regression_cifar.html
new file mode 100644
index 0000000000..8b78a81ba0
--- /dev/null
+++ b/2024.7/samples/logistictic_regression_cifar.html
@@ -0,0 +1,597 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+  <title>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn SVC for Adult dataset" href="svc_adult.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="knn_mnist.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
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+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+      </script>
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+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
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+</ul>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
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+
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/logistictic_regression_cifar.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Logistic-Regression-for-Cifar-dataset">
+<h1>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Logistic-Regression-for-Cifar-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+
+<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;CIFAR-100&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">43</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((54000, 3072), (6000, 3072), (54000,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
+    <span class="s2">&quot;solver&quot;</span><span class="p">:</span> <span class="s2">&quot;lbfgs&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;multi_class&quot;</span><span class="p">:</span> <span class="s2">&quot;multinomial&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 24.82 s&#39;
+</pre></div></div>
+</div>
+<p>Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">log_loss_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">log_loss</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn Log Loss: </span><span class="si">{</span><span class="n">log_loss_opt</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class LogisticRegression</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 395.03 s&#39;
+</pre></div></div>
+</div>
+<p>Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">log_loss_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">log_loss</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn Log Loss: </span><span class="si">{</span><span class="n">log_loss_original</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn Log Loss: 3.7140870590578428 s&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare Log Loss metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Log Loss metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">log_loss_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Log Loss metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">log_loss_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">log_loss_opt</span><span class="o">/</span><span class="n">log_loss_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="knn_mnist.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="svc_adult.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn SVC for Adult dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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diff --git a/2024.7/samples/logistictic_regression_cifar.ipynb b/2024.7/samples/logistictic_regression_cifar.ipynb
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2024.7/samples/logistictic_regression_cifar.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/nusvr_medical_charges.html b/2024.7/samples/nusvr_medical_charges.html
new file mode 100644
index 0000000000..58bf339a53
--- /dev/null
+++ b/2024.7/samples/nusvr_medical_charges.html
@@ -0,0 +1,606 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="next" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" href="random_forest_yolanda.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" href="linear_regression.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
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+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset</li>
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+            <a href="../_sources/samples/nusvr_medical_charges.ipynb.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-NuSVR-for-Medical-Charges-dataset">
+<h1>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-NuSVR-for-Medical-Charges-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;medical_charges_nominal&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to this heading"></a></h2>
+<p>Encode categorical features</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cat_columns</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">([</span><span class="s2">&quot;category&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span>
+<span class="n">x</span><span class="p">[</span><span class="n">cat_columns</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">cat_columns</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">codes</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((48919, 11), (114146, 11), (48919,), (114146,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">NuSVR</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;nu&quot;</span><span class="p">:</span> <span class="mf">0.4</span><span class="p">,</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="n">y_train</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
+    <span class="s2">&quot;degree&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s2">&quot;kernel&quot;</span><span class="p">:</span> <span class="s2">&quot;poly&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">nusvr</span> <span class="o">=</span> <span class="n">NuSVR</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 24.69 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_opt</span> <span class="o">=</span> <span class="n">nusvr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn R2 score: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn R2 score: 0.8635974264586637&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class NuSVR</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">NuSVR</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">nusvr</span> <span class="o">=</span> <span class="n">NuSVR</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 331.85 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the NuSVR algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_original</span> <span class="o">=</span> <span class="n">nusvr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn R2 score: </span><span class="si">{</span><span class="n">score_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn R2 score: 0.8636031741516902&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare R2 score of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;R2 score of patched Scikit-learn: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;R2 score of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">score_opt</span><span class="o">/</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="linear_regression.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="random_forest_yolanda.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+  <hr/>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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diff --git a/2024.7/samples/nusvr_medical_charges.ipynb b/2024.7/samples/nusvr_medical_charges.ipynb
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2024.7/samples/nusvr_medical_charges.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/random_forest_yolanda.html b/2024.7/samples/random_forest_yolanda.html
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index 0000000000..ad5517cf60
--- /dev/null
+++ b/2024.7/samples/random_forest_yolanda.html
@@ -0,0 +1,588 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+  <title>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" href="ridge_regression.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset" href="nusvr_medical_charges.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+      </script>
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+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
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+          
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+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Random Forest for Yolanda dataset</li>
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+            <a href="../_sources/samples/random_forest_yolanda.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Random-Forest-for-Yolanda-dataset">
+<h1>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Random-Forest-for-Yolanda-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Yolanda&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">72</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((280000, 100), (120000, 100), (280000,), (120000,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_estimators&quot;</span><span class="p">:</span> <span class="mi">150</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">44</span><span class="p">,</span> <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 42.56 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn Mean Squared Error: </span><span class="si">{</span><span class="n">mse_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class RandomForestRegressor.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 123.34 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Random Forest algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn Mean Squared Error: </span><span class="si">{</span><span class="n">mse_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn Mean Squared Error: 83.62232345666878&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_opt</span><span class="o">/</span><span class="n">mse_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="nusvr_medical_charges.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+      class="version-switcher__button"
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diff --git a/2024.7/samples/random_forest_yolanda.ipynb b/2024.7/samples/random_forest_yolanda.ipynb
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2024.7/samples/random_forest_yolanda.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/ridge_regression.html b/2024.7/samples/ridge_regression.html
new file mode 100644
index 0000000000..63e9390aba
--- /dev/null
+++ b/2024.7/samples/ridge_regression.html
@@ -0,0 +1,640 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+  <title>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset</li>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-Ridge-Regression-for-Airlines-DepDelay-dataset">
+<h1>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Ridge-Regression-for-Airlines-DepDelay-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Airlines_DepDelay_10M&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to this heading"></a></h2>
+<p>Let’s encode categorical features with LabelEncoder</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;UniqueCarrier&quot;</span><span class="p">,</span> <span class="s2">&quot;Origin&quot;</span><span class="p">,</span> <span class="s2">&quot;Dest&quot;</span><span class="p">]:</span>
+    <span class="n">le</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+    <span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((9000000, 9), (1000000, 9), (9000000,), (1000000,))
+</pre></div></div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_train</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="mf">0.3</span><span class="p">,</span>
+    <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.06 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 1.0014288520708046&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class Ridge</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 0.70 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Ridge Regression algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 1.0014288520708057&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="random_forest_yolanda.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
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+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/2024.7/samples/ridge_regression.ipynb b/2024.7/samples/ridge_regression.ipynb
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2024.7/samples/ridge_regression.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/svc_adult.html b/2024.7/samples/svc_adult.html
new file mode 100644
index 0000000000..f0b47c49e9
--- /dev/null
+++ b/2024.7/samples/svc_adult.html
@@ -0,0 +1,593 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn SVC for Adult dataset &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/tabs.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+      <script src="../_static/doctools.js"></script>
+      <script src="../_static/sphinx_highlight.js"></script>
+      <script src="../_static/clipboard.min.js"></script>
+      <script src="../_static/copybutton.js"></script>
+      <script src="../_static/version_switcher.js"></script>
+      <script src="../_static/design-tabs.js"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" href="ElasticNet.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset" href="logistictic_regression_cifar.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
+          (function () {
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+      </script>
+
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn SVC for Adult dataset</li>
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+            <a href="../_sources/samples/svc_adult.ipynb.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-SVC-for-Adult-dataset">
+<h1>Intel® Extension for Scikit-learn SVC for Adult dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-SVC-for-Adult-dataset" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;a9a&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">100.0</span><span class="p">,</span> <span class="s2">&quot;kernel&quot;</span><span class="p">:</span> <span class="s2">&quot;rbf&quot;</span><span class="p">,</span> <span class="s2">&quot;gamma&quot;</span><span class="p">:</span> <span class="s2">&quot;scale&quot;</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 14.08 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for Intel® extension for Scikit-learn SVC:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for Intel® extension for Scikit-learn SVC:
+              precision    recall  f1-score   support
+
+        -1.0       0.87      0.90      0.88      7414
+         1.0       0.64      0.58      0.61      2355
+
+    accuracy                           0.82      9769
+   macro avg       0.76      0.74      0.75      9769
+weighted avg       0.82      0.82      0.82      9769
+
+
+</pre></div></div>
+</div>
+<p><em>The first column of the classification report above is the class labels.</em></p>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class SVC.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the SVC algorithm with original Scikit-learn library for Adult dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 803.06 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the SVC algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for original Scikit-learn SVC:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for original Scikit-learn SVC:
+              precision    recall  f1-score   support
+
+        -1.0       0.87      0.90      0.88      7414
+         1.0       0.64      0.58      0.61      2355
+
+    accuracy                           0.82      9769
+   macro avg       0.76      0.74      0.75      9769
+weighted avg       0.82      0.82      0.82      9769
+
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h2&gt;With scikit-learn-intelex patching you can:&lt;/h2&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="logistictic_regression_cifar.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
+      </div>
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+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+</html>
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diff --git a/2024.7/samples/svc_adult.ipynb b/2024.7/samples/svc_adult.ipynb
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2024.7/samples/svc_adult.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/samples/tsne.html b/2024.7/samples/tsne.html
new file mode 100644
index 0000000000..2249cde1a5
--- /dev/null
+++ b/2024.7/samples/tsne.html
@@ -0,0 +1,567 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn TSNE example &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn TSNE example</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/tsne.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-TSNE-example">
+<h1>Intel® Extension for Scikit-learn TSNE example<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-TSNE-example" title="Permalink to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Generate-the-data">
+<h2>Generate the data<a class="headerlink" href="#Generate-the-data" title="Permalink to this heading"></a></h2>
+<div class="line-block">
+<div class="line">Generate isotropic Gaussian blobs for clustering. With the number of samples: 20k Number of features: 100 Number of blobs: 4 Source:</div>
+<div class="line"><a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html">https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html</a></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">20000</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">TSNE</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_components&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">tsne</span> <span class="o">=</span> <span class="n">TSNE</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+<span class="n">embedding_intelex</span> <span class="o">=</span> <span class="n">tsne</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">time_opt</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">time_opt</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® Extension for scikit-learn. Divergence: </span><span class="si">{</span><span class="n">tsne</span><span class="o">.</span><span class="n">kl_divergence_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Intel® extension for Scikit-learn time: 12.63 s
+Intel® Extension for scikit-learn. Divergence: 4.289110606110757
+</pre></div></div>
+</div>
+<p>### Train the same algorithm with original Scikit-learn In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class TSNE.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training algorithm with original Scikit-learn library for generated dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">TSNE</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_components&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">tsne</span> <span class="o">=</span> <span class="n">TSNE</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+<span class="n">embedding_original</span> <span class="o">=</span> <span class="n">tsne</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">time_original</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">time_original</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn. Divergence: </span><span class="si">{</span><span class="n">tsne</span><span class="o">.</span><span class="n">kl_divergence_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Original Scikit-learn time: 37.66 s
+Original Scikit-learn. Divergence: 4.2955403327941895
+</pre></div></div>
+</div>
+</section>
+<section id="Plot-embeddings-original-scikit-learn-and-Intel®-extension">
+<h2>Plot embeddings original scikit-learn and Intel® extension<a class="headerlink" href="#Plot-embeddings-original-scikit-learn-and-Intel®-extension" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">y</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">emb</span><span class="p">,</span> <span class="n">title</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+    <span class="p">[</span><span class="n">embedding_intelex</span><span class="p">,</span> <span class="n">embedding_original</span><span class="p">],</span>
+    <span class="p">[</span><span class="s2">&quot;Intel® Extension for scikit-learn&quot;</span><span class="p">,</span> <span class="s2">&quot;Original scikit-learn&quot;</span><span class="p">],</span>
+<span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">emb</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">emb</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">colors</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_tsne_15_0.png" src="../_images/samples_tsne_15_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_tsne_15_1.png" src="../_images/samples_tsne_15_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sa">f</span><span class="s2">&quot;Speedup for this run: </span><span class="si">{</span><span class="p">(</span><span class="n">time_original</span><span class="o">/</span><span class="n">time_opt</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Speedup for this run: 3.0&#39;
+</pre></div></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f03d593f510>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
+    Cookies
+</a>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
+    Privacy
+</a>
+<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
+    Do Not Share My Personal Information
+</a>
+
+  </div>
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+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/2024.7/samples/tsne.ipynb b/2024.7/samples/tsne.ipynb
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2024.7/samples/tsne.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2024.7/search.html b/2024.7/search.html
new file mode 100644
index 0000000000..00e46b42a9
--- /dev/null
+++ b/2024.7/search.html
@@ -0,0 +1,403 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Search &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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+
+  
+    <link rel="shortcut icon" href="_static/favicons.png"/>
+    
+      <script src="_static/jquery.js"></script>
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <script src="_static/searchtools.js"></script>
+    <script src="_static/language_data.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="#" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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diff --git a/2024.7/searchindex.js b/2024.7/searchindex.js
new file mode 100644
index 0000000000..e0337bea12
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+++ b/2024.7/searchindex.js
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
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+  <section id="intel-r-extension-for-scikit-learn-support">
+<h1>Intel(R) Extension for Scikit-learn Support<a class="headerlink" href="#intel-r-extension-for-scikit-learn-support" title="Permalink to this heading"></a></h1>
+<p>We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn.</p>
+<p>Use the following methods if you face any challenges.</p>
+<section id="issues">
+<h2>Issues<a class="headerlink" href="#issues" title="Permalink to this heading"></a></h2>
+<p>If you have a problem, check out the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">GitHub Issues</a> to see if the issue you want to address is already reported.</p>
+<p>You may find users that have encountered the same bug or have similar ideas for changes or updates.</p>
+<p>You can use issues to report a problem, make a feature request, or add comments on an existing issue.</p>
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+  <title>Intel® Extension for Scikit-learn* Tutorials &amp; Case Studies &mdash; Intel(R) Extension for Scikit-learn* 2024.7 documentation</title>
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@@ -95,6 +91,7 @@
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+  
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+    <script src="_static/js/theme.js"></script>
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+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
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+      <!-- dropdown will be populated by javascript on page load -->
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Verbose Mode</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/verbose.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="verbose-mode">
+<span id="verbose"></span><h1>Verbose Mode<a class="headerlink" href="#verbose-mode" title="Permalink to this heading"></a></h1>
+<p>Use Intel® Extension for Scikit-learn* in verbose mode to find out which implementation of the algorithm is currently used,
+Intel® Extension for Scikit-learn* or original Scikit-learn.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Verbose mode is only available for <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">supported algorithms</span></a>.</p>
+</div>
+<p>To enable verbose mode, set the <code class="docutils literal notranslate"><span class="pre">SKLEARNEX_VERBOSE</span></code> environment variable as shown below:</p>
+<ul>
+<li><p>On Linux* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
+</pre></div>
+</div>
+</li>
+<li><p>On Windows* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
+</pre></div>
+</div>
+</li>
+</ul>
+<p>Alternatively, get Intel® Extension for Scikit-learn* logger and set its logging level in the Python code:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span>
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s1">&#39;sklearnex&#39;</span><span class="p">)</span>
+<span class="n">logger</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>During the calls that use Intel-optimized scikit-learn, you will receive additional print statements
+that indicate which implementation is being called.
+These print statements are only available for <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">supported algorithms</span></a>.</p>
+<p>For example, for DBSCAN you get one of these print statements depending on which implementation is used:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">INFO</span><span class="p">:</span><span class="n">sklearnex</span><span class="p">:</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">DBSCAN</span><span class="o">.</span><span class="n">fit</span><span class="p">:</span> <span class="n">running</span> <span class="n">accelerated</span> <span class="n">version</span> <span class="n">on</span> <span class="n">CPU</span>
+</pre></div>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">INFO</span><span class="p">:</span><span class="n">sklearnex</span><span class="p">:</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">DBSCAN</span><span class="o">.</span><span class="n">fit</span><span class="p">:</span> <span class="n">fallback</span> <span class="n">to</span> <span class="n">original</span> <span class="n">Scikit</span><span class="o">-</span><span class="n">learn</span>
+</pre></div>
+</div>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="distributed-mode.html" class="btn btn-neutral float-left" title="Distributed Mode" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="deprecation.html" class="btn btn-neutral float-right" title="Deprecation Notice" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f03d55cfa90>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
+    Cookies
+</a>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
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+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2024.7"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
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+
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+# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb b/2025.0/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/dbscan.ipynb b/2025.0/.doctrees/nbsphinx/samples/dbscan.ipynb
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/dbscan.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/kmeans.ipynb b/2025.0/.doctrees/nbsphinx/samples/kmeans.ipynb
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/kmeans.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/knn_mnist.ipynb b/2025.0/.doctrees/nbsphinx/samples/knn_mnist.ipynb
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/knn_mnist.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/lasso_regression.ipynb b/2025.0/.doctrees/nbsphinx/samples/lasso_regression.ipynb
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/lasso_regression.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/linear_regression.ipynb b/2025.0/.doctrees/nbsphinx/samples/linear_regression.ipynb
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/linear_regression.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb b/2025.0/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb b/2025.0/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb b/2025.0/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/ridge_regression.ipynb b/2025.0/.doctrees/nbsphinx/samples/ridge_regression.ipynb
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/ridge_regression.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/svc_adult.ipynb b/2025.0/.doctrees/nbsphinx/samples/svc_adult.ipynb
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/svc_adult.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/.doctrees/nbsphinx/samples/tsne.ipynb b/2025.0/.doctrees/nbsphinx/samples/tsne.ipynb
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2025.0/.doctrees/nbsphinx/samples/tsne.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#unpatching">Unpatching</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
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+  <h1>All modules for which code is available</h1>
+<ul><li><a href="sklearnex/basic_statistics/basic_statistics.html">sklearnex.basic_statistics.basic_statistics</a></li>
+<li><a href="sklearnex/basic_statistics/incremental_basic_statistics.html">sklearnex.basic_statistics.incremental_basic_statistics</a></li>
+<li><a href="sklearnex/covariance/incremental_covariance.html">sklearnex.covariance.incremental_covariance</a></li>
+<li><a href="sklearnex/linear_model/incremental_linear.html">sklearnex.linear_model.incremental_linear</a></li>
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+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
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+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../guide/acceleration.html">Tuning Guide</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#random-forest">Random Forest</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">sklearnex.basic_statistics.basic_statistics</li>
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+             
+  <h1>Source code for sklearnex.basic_statistics.basic_statistics</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2023 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">_check_sample_weight</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.basic_statistics</span> <span class="kn">import</span> <span class="n">BasicStatistics</span> <span class="k">as</span> <span class="n">onedal_BasicStatistics</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">PatchingConditionsChain</span>
+
+
+<div class="viewcode-block" id="BasicStatistics">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics">[docs]</a>
+<span class="nd">@control_n_jobs</span><span class="p">(</span><span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;fit&quot;</span><span class="p">])</span>
+<span class="k">class</span> <span class="nc">BasicStatistics</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Estimator for basic statistics.</span>
+<span class="sd">    Allows to compute basic statistics for provided data.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    result_options: string or list, default=&#39;all&#39;</span>
+<span class="sd">        Used to set statistics to calculate. Possible values are ``&#39;min&#39;``, ``&#39;max&#39;``, ``&#39;sum&#39;``, ``&#39;mean&#39;``, ``&#39;variance&#39;``,</span>
+<span class="sd">        ``&#39;variation&#39;``, ``sum_squares&#39;``, ``sum_squares_centered&#39;``, ``&#39;standard_deviation&#39;``, ``&#39;second_order_raw_moment&#39;``</span>
+<span class="sd">        or a list containing any of these values. If set to ``&#39;all&#39;`` then all possible statistics will be</span>
+<span class="sd">        calculated.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">        min : ndarray of shape (n_features,)</span>
+<span class="sd">            Minimum of each feature over all samples.</span>
+<span class="sd">        max : ndarray of shape (n_features,)</span>
+<span class="sd">            Maximum of each feature over all samples.</span>
+<span class="sd">        sum : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of each feature over all samples.</span>
+<span class="sd">        mean : ndarray of shape (n_features,)</span>
+<span class="sd">            Mean of each feature over all samples.</span>
+<span class="sd">        variance : ndarray of shape (n_features,)</span>
+<span class="sd">            Variance of each feature over all samples.</span>
+<span class="sd">        variation : ndarray of shape (n_features,)</span>
+<span class="sd">            Variation of each feature over all samples.</span>
+<span class="sd">        sum_squares : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of squares for each feature over all samples.</span>
+<span class="sd">        standard_deviation : ndarray of shape (n_features,)</span>
+<span class="sd">            Standard deviation of each feature over all samples.</span>
+<span class="sd">        sum_squares_centered : ndarray of shape (n_features,)</span>
+<span class="sd">            Centered sum of squares for each feature over all samples.</span>
+<span class="sd">        second_order_raw_moment : ndarray of shape (n_features,)</span>
+<span class="sd">            Second order moment of each feature over all samples.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Attribute exists only if corresponding result option has been provided.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Some results can exhibit small variations due to</span>
+<span class="sd">    floating point error accumulation and multithreading.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.basic_statistics import BasicStatistics</span>
+<span class="sd">    &gt;&gt;&gt; bs = BasicStatistics(result_options=[&#39;sum&#39;, &#39;min&#39;, &#39;max&#39;])</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; bs.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; bs.sum_</span>
+<span class="sd">    np.array([4., 6.])</span>
+<span class="sd">    &gt;&gt;&gt; bs.min_</span>
+<span class="sd">    np.array([1., 2.])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">result_options</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">options</span> <span class="o">=</span> <span class="n">result_options</span>
+
+    <span class="n">_onedal_basic_statistics</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_BasicStatistics</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_save_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">options</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="n">onedal_BasicStatistics</span><span class="o">.</span><span class="n">get_all_result_options</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">options</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">result_options</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="p">,</span> <span class="n">result_options</span><span class="p">))</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">option</span> <span class="ow">in</span> <span class="n">result_options</span><span class="p">:</span>
+                <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">option</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="p">,</span> <span class="n">option</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearnex.basic_statistics.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_data</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span> <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">_check_sample_weight</span><span class="p">(</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">options</span><span class="p">,</span>
+        <span class="p">}</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_basic_statistics</span><span class="p">(</span><span class="o">**</span><span class="n">onedal_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="p">,</span> <span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_save_attributes</span><span class="p">()</span>
+
+<div class="viewcode-block" id="BasicStatistics.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Compute statistics with X, using minibatches of size batch_size.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Data for compute, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Weights for compute weighted statistics, where ``n_samples`` is the number of samples.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
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diff --git a/2025.0/_modules/sklearnex/basic_statistics/incremental_basic_statistics.html b/2025.0/_modules/sklearnex/basic_statistics/incremental_basic_statistics.html
new file mode 100644
index 0000000000..e1128ce7f0
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+
+
+<!DOCTYPE html>
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+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
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+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
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+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
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+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../license.html">License</a></li>
+</ul>
+
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../../index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">sklearnex.basic_statistics.incremental_basic_statistics</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for sklearnex.basic_statistics.incremental_basic_statistics</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span><span class="p">,</span> <span class="n">gen_batches</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">_check_sample_weight</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.basic_statistics</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IncrementalBasicStatistics</span> <span class="k">as</span> <span class="n">onedal_IncrementalBasicStatistics</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">PatchingConditionsChain</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">Interval</span><span class="p">,</span> <span class="n">StrOptions</span>
+
+<span class="kn">import</span> <span class="nn">numbers</span>
+
+
+<div class="viewcode-block" id="IncrementalBasicStatistics">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics">[docs]</a>
+<span class="nd">@control_n_jobs</span><span class="p">(</span><span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span> <span class="s2">&quot;_onedal_finalize_fit&quot;</span><span class="p">])</span>
+<span class="k">class</span> <span class="nc">IncrementalBasicStatistics</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates basic statistics on the given data, allows for computation when the data are split into</span>
+<span class="sd">    batches. The user can use ``partial_fit`` method to provide a single batch of data or use the ``fit`` method to provide</span>
+<span class="sd">    the entire dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    result_options: string or list, default=&#39;all&#39;</span>
+<span class="sd">        List of statistics to compute</span>
+
+<span class="sd">    batch_size : int, default=None</span>
+<span class="sd">        The number of samples to use for each batch. Only used when calling</span>
+<span class="sd">        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``</span>
+<span class="sd">        is inferred from the data and set to ``5 * n_features``.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">        min : ndarray of shape (n_features,)</span>
+<span class="sd">            Minimum of each feature over all samples.</span>
+
+<span class="sd">        max : ndarray of shape (n_features,)</span>
+<span class="sd">            Maximum of each feature over all samples.</span>
+
+<span class="sd">        sum : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of each feature over all samples.</span>
+
+<span class="sd">        mean : ndarray of shape (n_features,)</span>
+<span class="sd">            Mean of each feature over all samples.</span>
+
+<span class="sd">        variance : ndarray of shape (n_features,)</span>
+<span class="sd">            Variance of each feature over all samples.</span>
+
+<span class="sd">        variation : ndarray of shape (n_features,)</span>
+<span class="sd">            Variation of each feature over all samples.</span>
+
+<span class="sd">        sum_squares : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of squares for each feature over all samples.</span>
+
+<span class="sd">        standard_deviation : ndarray of shape (n_features,)</span>
+<span class="sd">            Standard deviation of each feature over all samples.</span>
+
+<span class="sd">        sum_squares_centered : ndarray of shape (n_features,)</span>
+<span class="sd">            Centered sum of squares for each feature over all samples.</span>
+
+<span class="sd">        second_order_raw_moment : ndarray of shape (n_features,)</span>
+<span class="sd">            Second order moment of each feature over all samples.</span>
+
+<span class="sd">        n_samples_seen_ : int</span>
+<span class="sd">            The number of samples processed by the estimator. Will be reset on</span>
+<span class="sd">            new calls to ``fit``, but increments across ``partial_fit`` calls.</span>
+
+<span class="sd">        batch_size_ : int</span>
+<span class="sd">            Inferred batch size from ``batch_size``.</span>
+
+<span class="sd">        n_features_in_ : int</span>
+<span class="sd">            Number of features seen during ``fit`` or  ``partial_fit``.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Attribute exists only if corresponding result option has been provided.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.basic_statistics import IncrementalBasicStatistics</span>
+<span class="sd">    &gt;&gt;&gt; incbs = IncrementalBasicStatistics(batch_size=1)</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.partial_fit(X[:1])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.partial_fit(X[1:])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.sum_</span>
+<span class="sd">    np.array([4., 6.])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.min_</span>
+<span class="sd">    np.array([1., 2.])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; incbs.sum_</span>
+<span class="sd">    np.array([4., 6.])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.max_</span>
+<span class="sd">    np.array([3., 4.])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_onedal_incremental_basic_statistics</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_IncrementalBasicStatistics</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="p">[</span>
+                <span class="n">StrOptions</span><span class="p">(</span>
+                    <span class="p">{</span>
+                        <span class="s2">&quot;all&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;min&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;max&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variance&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;standard_deviation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares_centered&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;second_order_raw_moment&quot;</span><span class="p">,</span>
+                    <span class="p">}</span>
+                <span class="p">),</span>
+                <span class="nb">list</span><span class="p">,</span>
+            <span class="p">],</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">result_options</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">result_options</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_basic_statistics</span><span class="o">.</span><span class="n">get_all_result_options</span><span class="p">()</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">=</span> <span class="n">result_options</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearn.basic_statistics.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="k">def</span> <span class="nf">_get_onedal_result_options</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">options</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">onedal_options</span> <span class="o">=</span> <span class="s2">&quot;|&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">result_options</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">onedal_options</span> <span class="o">=</span> <span class="n">options</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">onedal_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">options</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_finalize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">first_pass</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;n_samples_seen_&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">==</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_data</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">reset</span><span class="o">=</span><span class="n">first_pass</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">_check_sample_weight</span><span class="p">(</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">first_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">+=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_onedal_result_options</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">result_options</span><span class="p">)</span>
+        <span class="p">}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_basic_statistics</span><span class="p">(</span>
+                <span class="o">**</span><span class="n">onedal_params</span>
+            <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_data</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">_check_sample_weight</span><span class="p">(</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+
+        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">n_features</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">gen_batches</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span><span class="p">):</span>
+            <span class="n">X_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span>
+            <span class="n">weights_batch</span> <span class="o">=</span> <span class="n">sample_weight</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span> <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">(</span><span class="n">X_batch</span><span class="p">,</span> <span class="n">weights_batch</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attr</span><span class="p">):</span>
+        <span class="n">result_options</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;result_options&quot;</span><span class="p">]</span>
+        <span class="n">is_statistic_attr</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">attr</span> <span class="o">==</span> <span class="n">result_options</span><span class="p">)</span>
+        <span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">attr</span> <span class="ow">in</span> <span class="n">result_options</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">is_statistic_attr</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+            <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="p">,</span> <span class="n">attr</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">attr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">attr</span><span class="p">]</span>
+
+        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;</span><span class="si">{</span><span class="n">attr</span><span class="si">}</span><span class="s2">&#39;&quot;</span>
+        <span class="p">)</span>
+
+<div class="viewcode-block" id="IncrementalBasicStatistics.partial_fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Incremental fit with X. All of X is processed as a single batch.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Data for compute, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Weights for compute weighted statistics, where ``n_samples`` is the number of samples.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="IncrementalBasicStatistics.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Compute statistics with X, using minibatches of size batch_size.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Data for compute, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Weights for compute weighted statistics, where ``n_samples`` is the number of samples.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+</div>
+
+</pre></div>
+
+           </div>
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
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+      <li class="breadcrumb-item active">sklearnex.covariance.incremental_covariance</li>
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+  <h1>Source code for sklearnex.covariance.incremental_covariance</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ===============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ===============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">linalg</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">clone</span>
+<span class="kn">from</span> <span class="nn">sklearn.covariance</span> <span class="kn">import</span> <span class="n">EmpiricalCovariance</span> <span class="k">as</span> <span class="n">sklearn_EmpiricalCovariance</span>
+<span class="kn">from</span> <span class="nn">sklearn.covariance</span> <span class="kn">import</span> <span class="n">log_likelihood</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span><span class="p">,</span> <span class="n">gen_batches</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">_num_features</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">daal_check_version</span><span class="p">,</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.covariance</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IncrementalEmpiricalCovariance</span> <span class="k">as</span> <span class="n">onedal_IncrementalEmpiricalCovariance</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">config_context</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span><span class="p">,</span> <span class="n">wrap_output_data</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">PatchingConditionsChain</span><span class="p">,</span> <span class="n">register_hyperparameters</span>
+<span class="kn">from</span> <span class="nn">..metrics</span> <span class="kn">import</span> <span class="n">pairwise_distances</span>
+<span class="kn">from</span> <span class="nn">..utils._array_api</span> <span class="kn">import</span> <span class="n">get_namespace</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">Interval</span>
+
+
+<div class="viewcode-block" id="IncrementalEmpiricalCovariance">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance">[docs]</a>
+<span class="nd">@control_n_jobs</span><span class="p">(</span><span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span> <span class="s2">&quot;fit&quot;</span><span class="p">,</span> <span class="s2">&quot;_onedal_finalize_fit&quot;</span><span class="p">])</span>
+<span class="k">class</span> <span class="nc">IncrementalEmpiricalCovariance</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Maximum likelihood covariance estimator that allows for the estimation when the data are split into</span>
+<span class="sd">    batches. The user can use the ``partial_fit`` method to provide a single batch of data or use the ``fit`` method to provide</span>
+<span class="sd">    the entire dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    store_precision : bool, default=False</span>
+<span class="sd">        Specifies if the estimated precision is stored.</span>
+
+<span class="sd">    assume_centered : bool, default=False</span>
+<span class="sd">        If True, data are not centered before computation.</span>
+<span class="sd">        Useful when working with data whose mean is almost, but not exactly</span>
+<span class="sd">        zero.</span>
+<span class="sd">        If False (default), data are centered before computation.</span>
+
+<span class="sd">    batch_size : int, default=None</span>
+<span class="sd">        The number of samples to use for each batch. Only used when calling</span>
+<span class="sd">        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``</span>
+<span class="sd">        is inferred from the data and set to ``5 * n_features``, to provide a</span>
+<span class="sd">        balance between approximation accuracy and memory consumption.</span>
+
+<span class="sd">    copy : bool, default=True</span>
+<span class="sd">        If False, X will be overwritten. ``copy=False`` can be used to</span>
+<span class="sd">        save memory but is unsafe for general use.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    location_ : ndarray of shape (n_features,)</span>
+<span class="sd">        Estimated location, i.e. the estimated mean.</span>
+
+<span class="sd">    covariance_ : ndarray of shape (n_features, n_features)</span>
+<span class="sd">        Estimated covariance matrix</span>
+
+<span class="sd">    n_samples_seen_ : int</span>
+<span class="sd">        The number of samples processed by the estimator. Will be reset on</span>
+<span class="sd">        new calls to ``fit``, but increments across ``partial_fit`` calls.</span>
+
+<span class="sd">    batch_size_ : int</span>
+<span class="sd">        Inferred batch size from ``batch_size``.</span>
+
+<span class="sd">    n_features_in_ : int</span>
+<span class="sd">        Number of features seen during ``fit`` or ``partial_fit``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.covariance import IncrementalEmpiricalCovariance</span>
+<span class="sd">    &gt;&gt;&gt; inccov = IncrementalEmpiricalCovariance(batch_size=1)</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.partial_fit(X[:1])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.partial_fit(X[1:])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.covariance_</span>
+<span class="sd">    np.array([[1., 1.],[1., 1.]])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.location_</span>
+<span class="sd">    np.array([2., 3.])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; inccov.covariance_</span>
+<span class="sd">    np.array([[1., 1.],[1., 1.]])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.location_</span>
+<span class="sd">    np.array([2., 3.])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_onedal_incremental_covariance</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_IncrementalEmpiricalCovariance</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;store_precision&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;assume_centered&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+            <span class="s2">&quot;copy&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="n">get_precision</span> <span class="o">=</span> <span class="n">sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">get_precision</span>
+    <span class="n">error_norm</span> <span class="o">=</span> <span class="n">wrap_output_data</span><span class="p">(</span><span class="n">sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">error_norm</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">store_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">assume_centered</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">assume_centered</span> <span class="o">=</span> <span class="n">assume_centered</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">store_precision</span> <span class="o">=</span> <span class="n">store_precision</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">copy</span> <span class="o">=</span> <span class="n">copy</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearn.covariance.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_finalize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">daal_check_version</span><span class="p">((</span><span class="mi">2024</span><span class="p">,</span> <span class="s2">&quot;P&quot;</span><span class="p">,</span> <span class="mi">400</span><span class="p">))</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">assume_centered</span><span class="p">:</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">location_</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">covariance_</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">location</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">location</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">location_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">location</span><span class="p">))</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_precision</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">precision_</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">pinvh</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">covariance_</span><span class="p">,</span> <span class="n">check_finite</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">precision_</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">covariance_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">covariance_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;covariance_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">location_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">location_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;location_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+
+        <span class="n">first_pass</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;n_samples_seen_&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">==</span> <span class="mi">0</span>
+
+        <span class="c1"># finite check occurs on onedal side</span>
+        <span class="k">if</span> <span class="n">check_input</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_data</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">reset</span><span class="o">=</span><span class="n">first_pass</span><span class="p">,</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;method&quot;</span><span class="p">:</span> <span class="s2">&quot;dense&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;bias&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+            <span class="s2">&quot;assume_centered&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">assume_centered</span><span class="p">,</span>
+        <span class="p">}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_covariance</span><span class="p">(</span><span class="o">**</span><span class="n">onedal_params</span><span class="p">)</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">first_pass</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">+=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="k">finally</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="nd">@wrap_output_data</span>
+    <span class="k">def</span> <span class="nf">score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">xp</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">get_namespace</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+
+        <span class="n">location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">location_</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_data</span><span class="p">(</span>
+                <span class="n">X_test</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X_test</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;numpy&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">xp</span><span class="o">.</span><span class="vm">__name__</span><span class="p">:</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="n">xp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X_test</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+            <span class="c1"># depending on the sklearn version, check_array</span>
+            <span class="c1"># and validate_data will return only numpy arrays</span>
+            <span class="c1"># which will break dpnp/dpctl support. If the</span>
+            <span class="c1"># array namespace isn&#39;t from numpy and the data</span>
+            <span class="c1"># is now a numpy array, it has been validated and</span>
+            <span class="c1"># the original can be used.</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">X_test</span>
+
+        <span class="n">est</span> <span class="o">=</span> <span class="n">clone</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">est</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s2">&quot;assume_centered&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span>
+
+        <span class="c1"># test_cov is a numpy array, but calculated on device</span>
+        <span class="n">test_cov</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span> <span class="o">-</span> <span class="n">location</span><span class="p">)</span><span class="o">.</span><span class="n">covariance_</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">log_likelihood</span><span class="p">(</span><span class="n">test_cov</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_precision</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="n">res</span>
+
+<div class="viewcode-block" id="IncrementalEmpiricalCovariance.partial_fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Incremental fit with X. All of X is processed as a single batch.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where `n_samples` is the number of samples and</span>
+<span class="sd">            `n_features` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        check_input : bool, default=True</span>
+<span class="sd">            Run check_array on X.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">check_input</span><span class="o">=</span><span class="n">check_input</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="IncrementalEmpiricalCovariance.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fit the model with X, using minibatches of size batch_size.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where `n_samples` is the number of samples and</span>
+<span class="sd">            `n_features` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="c1"># finite check occurs on onedal side</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_data</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span> <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span> <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span> <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span> <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="k">else</span> <span class="mi">5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span>
+
+        <span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Only one sample available. You may want to reshape your data array&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">gen_batches</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span><span class="p">):</span>
+            <span class="n">X_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">(</span><span class="n">X_batch</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="c1"># expose sklearnex pairwise_distances if mahalanobis distance eventually supported</span>
+    <span class="k">def</span> <span class="nf">mahalanobis</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_check_feature_names</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">xp</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">get_namespace</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">precision</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_precision</span><span class="p">()</span>
+        <span class="c1"># compute mahalanobis distances</span>
+        <span class="c1"># pairwise_distances will check n_features (via n_feature matching with</span>
+        <span class="c1"># self.location_) , and will check for finiteness via check array</span>
+        <span class="c1"># check_feature_names will match _validate_data functionally</span>
+        <span class="n">location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">location_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span>
+        <span class="k">if</span> <span class="s2">&quot;numpy&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">xp</span><span class="o">.</span><span class="vm">__name__</span><span class="p">:</span>
+            <span class="c1"># Guarantee that inputs to pairwise_distances match in type and location</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="n">xp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">pairwise_distances</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">location</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;mahalanobis&quot;</span><span class="p">,</span> <span class="n">VI</span><span class="o">=</span><span class="n">precision</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="c1"># Throw the expected sklearn error in an n_feature length violation</span>
+            <span class="k">if</span> <span class="s2">&quot;Incompatible dimension for X and Y matrices: X.shape[1] ==&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;X has </span><span class="si">{</span><span class="n">_num_features</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="si">}</span><span class="s2"> features, but </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;is expecting </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span><span class="si">}</span><span class="s2"> features as input.&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="n">e</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">xp</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dist</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,)))</span> <span class="o">**</span> <span class="mi">2</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="n">mahalanobis</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">mahalanobis</span><span class="o">.</span><span class="vm">__doc__</span>
+    <span class="n">error_norm</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">error_norm</span><span class="o">.</span><span class="vm">__doc__</span>
+    <span class="n">score</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">score</span><span class="o">.</span><span class="vm">__doc__</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../../index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">sklearnex.linear_model.incremental_linear</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for sklearnex.linear_model.incremental_linear</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ===============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ===============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">MultiOutputMixin</span><span class="p">,</span> <span class="n">RegressorMixin</span>
+<span class="kn">from</span> <span class="nn">sklearn.exceptions</span> <span class="kn">import</span> <span class="n">NotFittedError</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">r2_score</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span><span class="p">,</span> <span class="n">gen_batches</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.linear_model</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IncrementalLinearRegression</span> <span class="k">as</span> <span class="n">onedal_IncrementalLinearRegression</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">Interval</span>
+
+<span class="kn">from</span> <span class="nn">onedal.common.hyperparameters</span> <span class="kn">import</span> <span class="n">get_hyperparameters</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span><span class="p">,</span> <span class="n">wrap_output_data</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">PatchingConditionsChain</span><span class="p">,</span> <span class="n">register_hyperparameters</span>
+
+
+<div class="viewcode-block" id="IncrementalLinearRegression">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression">[docs]</a>
+<span class="nd">@register_hyperparameters</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;fit&quot;</span><span class="p">:</span> <span class="n">get_hyperparameters</span><span class="p">(</span><span class="s2">&quot;linear_regression&quot;</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">),</span>
+        <span class="s2">&quot;partial_fit&quot;</span><span class="p">:</span> <span class="n">get_hyperparameters</span><span class="p">(</span><span class="s2">&quot;linear_regression&quot;</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">),</span>
+    <span class="p">}</span>
+<span class="p">)</span>
+<span class="nd">@control_n_jobs</span><span class="p">(</span>
+    <span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;fit&quot;</span><span class="p">,</span> <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span> <span class="s2">&quot;predict&quot;</span><span class="p">,</span> <span class="s2">&quot;_onedal_finalize_fit&quot;</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="k">class</span> <span class="nc">IncrementalLinearRegression</span><span class="p">(</span><span class="n">MultiOutputMixin</span><span class="p">,</span> <span class="n">RegressorMixin</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Trains a linear regression model, allows for computation if the data are split into</span>
+<span class="sd">    batches. The user can use the ``partial_fit`` method to provide a single batch of data or use the ``fit`` method to provide</span>
+<span class="sd">    the entire dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    fit_intercept : bool, default=True</span>
+<span class="sd">    Whether to calculate the intercept for this model. If set</span>
+<span class="sd">    to False, no intercept will be used in calculations</span>
+<span class="sd">    (i.e. data is expected to be centered).</span>
+
+<span class="sd">    copy_X : bool, default=True</span>
+<span class="sd">        If True, X will be copied; else, it may be overwritten.</span>
+
+<span class="sd">    n_jobs : int, default=None</span>
+<span class="sd">        The number of jobs to use for the computation.</span>
+
+<span class="sd">    batch_size : int, default=None</span>
+<span class="sd">        The number of samples to use for each batch. Only used when calling</span>
+<span class="sd">        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``</span>
+<span class="sd">        is inferred from the data and set to ``5 * n_features``.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    coef_ : array of shape (n_features, ) or (n_targets, n_features)</span>
+<span class="sd">        Estimated coefficients for the linear regression problem.</span>
+<span class="sd">        If multiple targets are passed during the fit (y 2D), this</span>
+<span class="sd">        is a 2D array of shape (n_targets, n_features), while if only</span>
+<span class="sd">        one target is passed, this is a 1D array of length n_features.</span>
+
+<span class="sd">    intercept_ : float or array of shape (n_targets,)</span>
+<span class="sd">        Independent term in the linear model. Set to 0.0 if</span>
+<span class="sd">        `fit_intercept = False`.</span>
+
+<span class="sd">    n_samples_seen_ : int</span>
+<span class="sd">        The number of samples processed by the estimator. Will be reset on</span>
+<span class="sd">        new calls to ``fit``, but increments across ``partial_fit`` calls.</span>
+<span class="sd">        It should be not less than `n_features_in_` if `fit_intercept`</span>
+<span class="sd">        is False and not less than `n_features_in_` + 1 if `fit_intercept`</span>
+<span class="sd">        is True to obtain regression coefficients.</span>
+
+<span class="sd">    batch_size_ : int</span>
+<span class="sd">        Inferred batch size from ``batch_size``.</span>
+
+<span class="sd">    n_features_in_ : int</span>
+<span class="sd">        Number of features seen during ``fit`` or ``partial_fit``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.linear_model import IncrementalLinearRegression</span>
+<span class="sd">    &gt;&gt;&gt; inclr = IncrementalLinearRegression(batch_size=2)</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4], [5, 6], [7, 10]])</span>
+<span class="sd">    &gt;&gt;&gt; y = np.array([1.5, 3.5, 5.5, 8.5])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.partial_fit(X[:2], y[:2])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.partial_fit(X[2:], y[2:])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.coef_</span>
+<span class="sd">    np.array([0.5., 0.5.])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.intercept_</span>
+<span class="sd">    np.array(0.)</span>
+<span class="sd">    &gt;&gt;&gt; inclr.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; inclr.coef_</span>
+<span class="sd">    np.array([0.5., 0.5.])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.intercept_</span>
+<span class="sd">    np.array(0.)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_onedal_incremental_linear</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_IncrementalLinearRegression</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">fit_intercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">copy_X</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span> <span class="o">=</span> <span class="n">fit_intercept</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span> <span class="o">=</span> <span class="n">copy_X</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearn.linear_model.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_data</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">r2_score</span><span class="p">(</span>
+            <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_predict</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">),</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">first_pass</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;n_samples_seen_&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">==</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">check_input</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+                <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_data</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">y</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">reset</span><span class="o">=</span><span class="n">first_pass</span><span class="p">,</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                    <span class="n">multi_output</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">y</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">first_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">+=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span><span class="p">,</span> <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_linear</span><span class="p">(</span><span class="o">**</span><span class="n">onedal_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_finalize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="n">is_underdetermined</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="n">is_underdetermined</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Not enough samples to finalize&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_data</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">y</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                <span class="n">multi_output</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">y</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="n">is_underdetermined</span> <span class="o">=</span> <span class="n">n_samples</span> <span class="o">&lt;</span> <span class="n">n_features</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">is_underdetermined</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Not enough samples to run oneDAL backend&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">n_features</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">gen_batches</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span><span class="p">):</span>
+            <span class="n">X_batch</span><span class="p">,</span> <span class="n">y_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">batch</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">(</span><span class="n">X_batch</span><span class="p">,</span> <span class="n">y_batch</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="c1"># finite check occurs on onedal side</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">n_features</span>
+
+        <span class="k">if</span> <span class="n">n_samples</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Only one sample available. You may want to reshape your data array&quot;</span>
+            <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="nf">get_intercept_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">intercept_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;intercept_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_intercept_</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;intercept_&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">intercept_</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_onedal_model</span>
+
+    <span class="k">def</span> <span class="nf">get_coef_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">coef_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;coef_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_coef_</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;coef_&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">coef_</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_onedal_model</span>
+
+    <span class="n">coef_</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">get_coef_</span><span class="p">,</span> <span class="n">set_coef_</span><span class="p">)</span>
+    <span class="n">intercept_</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">get_intercept_</span><span class="p">,</span> <span class="n">set_intercept_</span><span class="p">)</span>
+
+<div class="viewcode-block" id="IncrementalLinearRegression.partial_fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Incremental fit linear model with X and y. All of X and y is</span>
+<span class="sd">        processed as a single batch.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            `n_features` is the number of features.</span>
+
+<span class="sd">        y : array-like of shape (n_samples,) or (n_samples, n_targets)</span>
+<span class="sd">            Target values, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_targets`` is the number of targets.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">y</span><span class="p">,</span>
+            <span class="n">check_input</span><span class="o">=</span><span class="n">check_input</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="IncrementalLinearRegression.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fit the model with X and y, using minibatches of size ``batch_size``.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features. It is necessary for</span>
+<span class="sd">            ``n_samples`` to be not less than ``n_features`` if ``fit_intercept``</span>
+<span class="sd">            is False and not less than ``n_features + 1`` if ``fit_intercept``</span>
+<span class="sd">            is True</span>
+
+<span class="sd">        y : array-like of shape (n_samples,) or (n_samples, n_targets)</span>
+<span class="sd">            Target values, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_targets`` is the number of targets.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">y</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="IncrementalLinearRegression.predict">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict">[docs]</a>
+    <span class="nd">@wrap_output_data</span>
+    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Predict using the linear model.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like or sparse matrix, shape (n_samples, n_features)</span>
+<span class="sd">            Samples.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        C : array, shape (n_samples, n_targets)</span>
+<span class="sd">            Returns predicted values.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;coef_&quot;</span><span class="p">):</span>
+            <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="s2">&quot;This </span><span class="si">%(name)s</span><span class="s2"> instance is not fitted yet. Call &#39;fit&#39; or &#39;partial_fit&#39; &quot;</span>
+                <span class="s2">&quot;with appropriate arguments before using this estimator.&quot;</span>
+            <span class="p">)</span>
+            <span class="k">raise</span> <span class="n">NotFittedError</span><span class="p">(</span><span class="n">msg</span> <span class="o">%</span> <span class="p">{</span><span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">})</span>
+
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;predict&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_predict</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="nd">@wrap_output_data</span>
+    <span class="k">def</span> <span class="nf">score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the coefficient of determination of the prediction.</span>
+
+<span class="sd">        The coefficient of determination :math:`R^2` is defined as</span>
+<span class="sd">        :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual</span>
+<span class="sd">        sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`</span>
+<span class="sd">        is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.</span>
+<span class="sd">        The best possible score is 1.0 and it can be negative (because the</span>
+<span class="sd">        model can be arbitrarily worse). A constant model that always predicts</span>
+<span class="sd">        the expected value of `y`, disregarding the input features, would get</span>
+<span class="sd">        a :math:`R^2` score of 0.0.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Test samples. For some estimators this may be a precomputed</span>
+<span class="sd">            kernel matrix or a list of generic objects instead with shape</span>
+<span class="sd">            ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``</span>
+<span class="sd">            is the number of samples used in the fitting for the estimator.</span>
+
+<span class="sd">        y : array-like of shape (n_samples,) or (n_samples, n_outputs)</span>
+<span class="sd">            True values for `X`.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Sample weights.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        score : float</span>
+<span class="sd">            :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        The :math:`R^2` score used when calling ``score`` on a regressor uses</span>
+<span class="sd">        ``multioutput=&#39;uniform_average&#39;`` from version 0.23 to keep consistent</span>
+<span class="sd">        with default value of :func:`~sklearn.metrics.r2_score`.</span>
+<span class="sd">        This influences the ``score`` method of all the multioutput</span>
+<span class="sd">        regressors (except for</span>
+<span class="sd">        :class:`~sklearn.multioutput.MultiOutputRegressor`).</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;coef_&quot;</span><span class="p">):</span>
+            <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="s2">&quot;This </span><span class="si">%(name)s</span><span class="s2"> instance is not fitted yet. Call &#39;fit&#39; or &#39;partial_fit&#39; &quot;</span>
+                <span class="s2">&quot;with appropriate arguments before using this estimator.&quot;</span>
+            <span class="p">)</span>
+            <span class="k">raise</span> <span class="n">NotFittedError</span><span class="p">(</span><span class="n">msg</span> <span class="o">%</span> <span class="p">{</span><span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">})</span>
+
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;score&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_score</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">y</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f27a47baa10>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
+    Cookies
+</a>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
+    Privacy
+</a>
+<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
+    Do Not Share My Personal Information
+</a>
+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/2025.0/_sources/404.rst.txt b/2025.0/_sources/404.rst.txt
new file mode 100644
index 0000000000..3060a3f9cc
--- /dev/null
+++ b/2025.0/_sources/404.rst.txt
@@ -0,0 +1,28 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+:orphan:
+:nosearch:
+
+Page not found
+==============
+
+Unfortunately, we could not find the page you were looking for. Try:
+
+- using :ref:`search <search>` to browse documentation
+- checking the table of contents on the left
+- filing an `issue <https://github.com/intel/scikit-learn-intelex/issues>`_ or
+  starting a `discussion <https://github.com/intel/scikit-learn-intelex/discussions>`_ on GitHub
\ No newline at end of file
diff --git a/2025.0/_sources/algorithms.rst.txt b/2025.0/_sources/algorithms.rst.txt
new file mode 100644
index 0000000000..4d165c1910
--- /dev/null
+++ b/2025.0/_sources/algorithms.rst.txt
@@ -0,0 +1,543 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _sklearn_algorithms:
+
+####################
+Supported Algorithms
+####################
+
+Applying |intelex| impacts the following scikit-learn algorithms:
+
+on CPU
+------
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVC`
+     - All parameters are supported
+     - No limitations
+   * - `NuSVC`
+     - All parameters are supported
+     - No limitations
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - 
+       - For ``algorithm`` == `'kd_tree'`:
+       
+         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - For ``algorithm`` == `'brute'`:
+         
+         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Multi-output and sparse data are not supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` not in [`'lbfgs'`, `'newton-cg'`]
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVR`
+     - All parameters are supported
+     - No limitations
+   * - `NuSVR`
+     - All parameters are supported
+     - No limitations
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Multi-output and sparse data are not supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported.
+   * - `Ridge`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``solver`` != `'auto'`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported, `#observations` should be >= `#features`.
+   * - `ElasticNet`
+     - All parameters are supported except:
+
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.
+   * - `Lasso`
+     - All parameters are supported except:
+
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+     - No limitations
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality Reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+     - Sparse data is not supported
+   * - `IncrementalPCA`
+     - All parameters are supported
+     - Sparse data is not supported
+   * - `TSNE`
+     - All parameters are supported except:
+
+       - ``metric`` != 'euclidean' or `'minkowski'` with ``p`` != `2`
+
+       Refer to :ref:`TSNE acceleration details <acceleration_tsne>` to learn more.
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - 
+       - For ``algorithm`` == 'kd_tree':
+         
+         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - For ``algorithm`` == 'brute':
+         
+         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Sparse data is not supported
+
+Other Tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `EmpiricalCovariance`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `train_test_split`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `assert_all_finite`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `pairwise_distance`
+     - All parameters are supported except:
+     
+       - ``metric`` not in [`'cosine'`, `'correlation'`]
+     - Only dense data is supported
+   * - `roc_auc_score`
+     - All parameters are supported except:
+       
+       - ``average`` != `None`
+       - ``sample_weight`` != `None`
+       - ``max_fpr`` != `None`
+       - ``multi_class`` != `None`
+     - No limitations
+
+on GPU
+------
+
+.. seealso:: :ref:`oneapi_gpu`
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVC`
+     - All parameters are supported except:
+
+       - ``kernel`` = `'sigmoid_poly'`
+       - ``class_weight`` != `None`
+     - Only binary dense data is supported
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` != `'newton-cg'`
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+       - ``penalty`` != `'l2'`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Only dense data is supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+       - ``Init`` = `'k-means++'` fallbacks to CPU.
+     - Sparse data is not supported
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality Reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+     
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+
+Other Tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `EmpiricalCovariance`
+     - All parameters are supported
+     - Only dense data is supported
+
+SPMD Support
+------------
+
+.. seealso:: :ref:`distributed`
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+       - ``predict_proba`` method not supported
+     - Only dense data is supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` != `'newton-cg'`
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+       - ``penalty`` != `'l2'`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Only dense data is supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+       - ``Init`` = `'k-means++'` fallbacks to CPU.
+     - Sparse data is not supported
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality Reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+     
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+       - ``fit`` is the only method supported
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+
+Other Tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `EmpiricalCovariance`
+     - All parameters are supported
+     - Only dense data is supported
+
+Scikit-learn Tests
+------------------
+
+Monkey-patched scikit-learn classes and functions passes scikit-learn's own test
+suite, with few exceptions, specified in `deselected_tests.yaml
+<https://github.com/intel/scikit-learn-intelex/blob/main/deselected_tests.yaml>`__.
diff --git a/2025.0/_sources/array_api.rst.txt b/2025.0/_sources/array_api.rst.txt
new file mode 100644
index 0000000000..4c71e0cb88
--- /dev/null
+++ b/2025.0/_sources/array_api.rst.txt
@@ -0,0 +1,123 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _array_api:
+
+=================
+Array API support
+=================
+The `Array API <https://data-apis.org/array-api/latest/>`_ specification defines
+a standard API for all array manipulation libraries with a NumPy-like API.
+Intel(R) Extension for Scikit-Learn doesn't require
+`array-api-compat <https://github.com/data-apis/array-api-compat>`__ to be installed for
+functional support of the array API standard.
+In the current implementation, the functional support of array api follows the functional
+support of different array or DataFrame inputs and does not modify the precision of the
+input and output data formats unless necessary. Any array API input will be converted to host
+numpy.ndarrays and all internal manipulations with data will be done with these representations of
+the input data. DPNP's 'ndarray' and Data Parallel Control's 'usm_ndarray' have special handling
+requirements that are described in the relevant section of this document. Output values will in
+all relevant cases match the input data format.
+
+.. note::
+    Currently, only `array-api-strict <https://github.com/data-apis/array-api-strict>`__,
+    `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__, `dpnp <https://github.com/IntelPython/dpnp>`__
+    and `numpy <https://numpy.org/>`__ are known to work with sklearnex estimators.
+.. note::
+    Stock Scikit-learn’s array API support requires `array-api-compat <https://github.com/data-apis/array-api-compat>`__ to be installed.
+
+
+Support for DPNP and DPCTL
+==========================
+The functional support of input data for sklearnex estimators also extended for SYCL USM array types.
+These include SYCL USM arrays `dpnp's <https://github.com/IntelPython/dpnp>`__ ndarray and
+`Data Parallel Control usm_ndarray <https://intelpython.github.io/dpctl/latest/index.html>`__.
+DPNP ndarray and Data Parallel Control usm_ndarray contain SYCL contexts which can be used for
+`sklearnex` device offloading.
+
+.. note::
+    Current support for DPNP and DPCTL usm_ndarray data can be copied and moved to and from device in sklearnex and have
+    impacts on memory utilization.
+
+DPCTL or DPNP inputs are not required to use `config_context(target_offload=device)`.
+`sklearnex` will use input usm_ndarray sycl context for device offloading.
+
+.. note::
+    As DPCTL or DPNP inputs contain SYCL contexts, they do not require `config_context(target_offload=device)`.
+    However, the use of `config_context`` will override the contained SYCL context and will force movement
+    of data to the targeted device.
+
+
+Support for Array API-compatible inputs
+=======================================
+All patched estimators, metrics, tools and non-scikit-learn estimators functionally support Array API standard.
+Intel(R) Extension for scikit-Learn preserves input data format for all outputs. For all array inputs except
+SYCL USM arrays `dpnp's <https://github.com/IntelPython/dpnp>`__ ndarray and
+`Data Parallel Control usm_ndarray <https://intelpython.github.io/dpctl/latest/index.html>`__ all computation
+will be only accomplished on CPU unless specified by a `config_context`` with an available GPU device.
+
+Stock scikit-learn uses `config_context(array_api_dispatch=True)` for enabling Array API
+`support <https://scikit-learn.org/1.5/modules/array_api.html>`__.
+If `array_api_dispatch` is enabled and the installed Scikit-Learn version supports array API, then the original
+inputs are used when falling back to Scikit-Learn functionality.
+
+.. note::
+    Data Parallel Control usm_ndarray or DPNP ndarray inputs will use host numpy data copies when
+    falling back to Scikit-Learn since they are not array API compliant.
+.. note::
+    Functional support doesn't guarantee that after the model is trained, fitted attributes that are arrays
+    will also be from the same namespace as the training data.
+
+
+Example usage
+=============
+
+DPNP ndarrays
+-------------
+
+Here is an example code to demonstrate how to use `dpnp <https://github.com/IntelPython/dpnp>`__ arrays to
+run `RandomForestRegressor` on a GPU without `config_context(array_api_dispatch=True)`:
+
+.. literalinclude:: ../../examples/sklearnex/random_forest_regressor_dpnp.py
+	   :language: python
+
+
+.. note::
+    Functional support doesn't guarantee that after the model is trained, fitted attributes that are arrays
+    will also be from the same namespace as the training data.
+
+For example, if `dpnp's <https://github.com/IntelPython/dpnp>`__ namespace was used for training,
+then fitted attributes will be on the CPU and `numpy.ndarray` data format.
+
+DPCTL usm_ndarrays
+------------------
+Here is an example code to demonstrate how to use `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__
+arrays to run `RandomForestClassifier` on a GPU witout `config_context(array_api_dispatch=True)`:
+
+.. literalinclude:: ../../examples/sklearnex/random_forest_classifier_dpctl.py
+	   :language: python
+
+As on previous example, if `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__ Array API namespace was
+used for training, then fitted attributes will be on the CPU and `numpy.ndarray` data format.
+
+Use of `array-api-strict`
+-------------------------
+
+Here is an example code to demonstrate how to use `array-api-strict <https://github.com/data-apis/array-api-strict>`__
+arrays to run `DBSCAN`.
+
+.. literalinclude:: ../../examples/sklearnex/dbscan_array_api.py
+	   :language: python
diff --git a/2025.0/_sources/blogs.rst.txt b/2025.0/_sources/blogs.rst.txt
new file mode 100644
index 0000000000..2d9b0024e5
--- /dev/null
+++ b/2025.0/_sources/blogs.rst.txt
@@ -0,0 +1,35 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _blogs:
+
+Follow us on Medium
+--------------------
+We publish blogs on Medium, so `follow us <https://medium.com/intel-analytics-software/tagged/machine-learning>`_
+to learn tips and tricks for more efficient data analysis the help of |intelex|.
+Here are our latest blogs:
+
+- `Save Time and Money with Intel Extension for Scikit-learn <https://medium.com/intel-analytics-software/save-time-and-money-with-intel-extension-for-scikit-learn-33627425ae4>`_,
+- `Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors <https://medium.com/intel-analytics-software/superior-machine-learning-performance-on-the-latest-intel-xeon-scalable-processor-efdec279f5a3>`_,
+- `Leverage Intel Optimizations in Scikit-Learn <https://medium.com/intel-analytics-software/leverage-intel-optimizations-in-scikit-learn-f562cb9d5544>`_,
+- `Intel Gives Scikit-Learn the Performance Boost Data Scientists Need <https://medium.com/intel-analytics-software/intel-gives-scikit-learn-the-performance-boost-data-scientists-need-42eb47c80b18>`_,
+- `From Hours to Minutes: 600x Faster SVM <https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae>`_,
+- `Improve the Performance of XGBoost and LightGBM Inference <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_,
+- `Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit <https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a>`_,
+- `Accelerate Your scikit-learn Applications <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_,
+- `Accelerate Linear Models for Machine Learning <https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe>`_,
+- `Accelerate K-Means Clustering <https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1>`_.
+- `Why Pay More for Machine Learning? <https://medium.com/intel-analytics-software/why-pay-more-for-machine-learning-893683bd78e4>`_.
diff --git a/2025.0/_sources/contribute.rst.txt b/2025.0/_sources/contribute.rst.txt
new file mode 100644
index 0000000000..28283bd9d8
--- /dev/null
+++ b/2025.0/_sources/contribute.rst.txt
@@ -0,0 +1,64 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+##################
+How to Contribute
+##################
+
+As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn. 
+This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.
+
+Licensing
+----------------------------------
+Intel(R) Extension for Scikit-learn uses the `Apache 2.0 License <https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE>`_. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.
+
+Pull Requests
+----------------------------------
+No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.
+
+Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:
+
+::
+
+  git config --global user.name "Kate Developer"
+  git config --global user.email kate.developer@company.com
+
+Before Contributing Changes
+********************************************
+
+Make sure you can build the product and run all the tests with your patch.
+For a larger feature, provide a relevant test.
+Document your code. Our project uses reStructuredText for documentation.
+For new file(s), specify the appropriate copyright year in the first line.
+Submit a pull request into the master branch.
+Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.
+
+Code Style
+-------------------
+
+We use `black <https://black.readthedocs.io/en/stable/>`_ and `isort <https://pycqa.github.io/isort/>`_ formatters for Python* code. 
+The line length is 90 characters; use default options otherwise. You can find the linter configuration in `.pyproject.toml <https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml>`_.
+
+A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.
+
+Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:
+
+::
+
+  pip install pre-commit
+  pre-commit install
+
+
diff --git a/2025.0/_sources/deprecation.rst.txt b/2025.0/_sources/deprecation.rst.txt
new file mode 100644
index 0000000000..5bc3f3fd19
--- /dev/null
+++ b/2025.0/_sources/deprecation.rst.txt
@@ -0,0 +1,30 @@
+.. ******************************************************************************
+.. * Copyright 2023 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+
+Deprecation Notice
+==================
+
+This page provides information about the deprecations of a specific |intelex| functionality. 
+
+macOS* Support
+**************
+
+**Deprecation:** macOS* support is deprecated for |intelex|. The 2023.x releases are the last to provide it. 
+
+**Reasons for deprecation:** No modern X86 macOS*-based systems are to be released.
+
+**Alternatives:** The 2023.x version on macOS*. 
diff --git a/2025.0/_sources/distributed-mode.rst.txt b/2025.0/_sources/distributed-mode.rst.txt
new file mode 100644
index 0000000000..cfeee2c611
--- /dev/null
+++ b/2025.0/_sources/distributed-mode.rst.txt
@@ -0,0 +1,52 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _distributed:
+
+Distributed Mode
+================
+
+|intelex| offers Single Program, Multiple Data (SPMD) supported interfaces for distributed computing.
+Several `GPU-supported algorithms <https://intel.github.io/scikit-learn-intelex/latest/oneapi-gpu.html#>`_ 
+also provide distributed, multi-GPU computing capabilities via integration with ``mpi4py``. The prerequisites 
+match those of GPU computing, along with an MPI backend of your choice (`Intel MPI recommended 
+<https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html#gs.dcan6r>`_, available 
+via ``impi-devel`` python package) and the ``mpi4py`` python package. If using |intelex| 
+`installed from sources <https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md#build-from-sources>`_,
+ensure that the spmd_backend is built.
+
+Note that |intelex| now supports GPU offloading to speed up MPI operations. This is supported automatically with 
+some MPI backends, but in order to use GPU offloading with Intel MPI, set the following environment variable (providing
+data on device without this may lead to a runtime error):
+ 
+::
+
+     export I_MPI_OFFLOAD=1
+
+Estimators can be imported from the ``sklearnex.spmd`` module. Data should be distributed across multiple nodes as 
+desired, and should be transfered to a dpctl or dpnp array before being passed to the estimator. View a full 
+example of this process in the |intelex| repository, where many examples of our SPMD-supported estimators are 
+available: https://github.com/intel/scikit-learn-intelex/blob/main/examples/sklearnex/. To run:
+
+::
+
+  mpirun -n 4 python linear_regression_spmd.py
+
+Note that additional mpirun arguments can be added as desired. SPMD-supported estimators are listed in the 
+`algorithms support documentation <https://intel.github.io/scikit-learn-intelex/latest/algorithms.html#spmd-support>`_.
+
+Additionally, daal4py offers some distributed functionality, see 
+`documentation <https://intelpython.github.io/daal4py/scaling.html>`_ for further details.
diff --git a/2025.0/_sources/guide/acceleration.rst.txt b/2025.0/_sources/guide/acceleration.rst.txt
new file mode 100644
index 0000000000..952ee73539
--- /dev/null
+++ b/2025.0/_sources/guide/acceleration.rst.txt
@@ -0,0 +1,86 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+########################
+Tuning Guide
+########################
+
+The performance of some algorithms changes based on the parameters that are used.
+This section denotes the details of such cases.
+
+Refer to :ref:`sklearn_algorithms` to see the full list of algorithms, parameters, and data formats supported in |intelex|.
+
+.. _acceleration_tsne:
+
+TSNE
+----
+
+TSNE algorithm consists of two components: KNN and Gradient Descent.
+The overall acceleration of TSNE depends on the acceleration of each of these algorithms.
+
+- The KNN part of the algorithm supports all parameters except:
+
+  - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+- The Gradient Descent part of the algorithm supports all parameters except:
+
+  - ``n_components`` = `3`
+  - ``method`` = `'exact'`
+  - ``verbose`` != `0`
+
+To get better performance, use parameters supported by both components.
+
+.. _acceleration_rf:
+
+Random Forest
+-------------
+
+Random Forest models accelerated with |intelex| and using the `hist` splitting
+method discretize training data by creating a histogram with a configurable
+number of bins. The following keyword arguments can be used to influence the
+created histogram.
+
+.. list-table::
+   :widths: 10 10 10 30
+   :header-rows: 1
+   :align: left
+
+   * - Keyword argument
+     - Possible values
+     - Default value
+     - Description
+   * - ``maxBins``
+     - `[0, inf)`
+     - ``256``
+     - Number of bins in the histogram with the discretized training data. The
+       value ``0`` disables data discretization.
+   * - ``minBinSize``
+     - `[1, inf)`
+     - ``5``
+     - Minimum number of training data points in each bin after discretization.
+   * - ``binningStrategy``
+     - ``quantiles, averages``
+     - ``quantiles``
+     - Selects the algorithm used to calculate bin edges. ``quantiles``
+       results in bins with a similar amount of training data points. ``averages``
+       divides the range of values observed in the training data set into
+       equal-width bins of size `(max - min) / maxBins`.
+
+Note that using discretized training data can greatly accelerate model training
+times, especially for larger data sets. However, due to the reduced fidelity of
+the data, the resulting model can present worse performance metrics compared to
+a model trained on the original data. In such cases, the number of bins can be
+increased with the ``maxBins`` parameter, or binning can be disabled entirely by
+setting ``maxBins=0``.
diff --git a/2025.0/_sources/index.rst.txt b/2025.0/_sources/index.rst.txt
new file mode 100644
index 0000000000..4663cd1d5b
--- /dev/null
+++ b/2025.0/_sources/index.rst.txt
@@ -0,0 +1,138 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |intelex_repo| replace:: |intelex| repository
+.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
+
+.. _index:
+
+#########
+|intelex|
+#########
+
+Intel(R) Extension for Scikit-learn is a **free software AI accelerator** designed to deliver up to **100X** faster performance for your existing scikit-learn code.
+The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.
+
+.. rubric:: Designed for Data Scientists and Framework Designers
+
+
+Use Intel(R) Extension for Scikit-learn, to:
+
+* Speed up training and inference by up to 100x with the equivalent mathematical accuracy
+* Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs
+* Integrate the extension into your existing Scikit-learn applications without code modifications
+* Enable and disable the extension with a couple of lines of code or at the command line
+
+Intel(R) Extension for Scikit-learn is also a part of `Intel(R) AI Tools <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html>`_.
+
+
+.. image:: _static/scikit-learn-acceleration.PNG
+  :width: 800
+
+
+These performance charts use benchmarks that you can find in the `scikit-learn bench repository <https://github.com/IntelPython/scikit-learn_bench>`_. 
+
+
+Supported Algorithms 
+---------------------
+
+See all of the :ref:`sklearn_algorithms`. 
+
+
+Intel(R) Optimizations
+----------------------------------
+
+Enable Intel(R) CPU Optimizations
+*********************************
+
+::
+
+   import numpy as np
+   from sklearnex import patch_sklearn
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+Enable Intel(R) GPU optimizations
+*********************************
+::
+
+   import numpy as np
+   import dpctl
+   from sklearnex import patch_sklearn, config_context
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   with config_context(target_offload="gpu:0"):
+       clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+
+
+.. toctree::
+   :caption: Get Started
+   :hidden:
+   :maxdepth: 3
+
+   quick-start.rst
+   samples.rst
+   kaggle.rst
+   
+.. toctree::
+   :caption: Developer Guide
+   :hidden:
+   :maxdepth: 2
+
+   algorithms.rst
+   oneAPI and GPU support <oneapi-gpu.rst>
+   distributed-mode.rst
+   non-scikit-algorithms.rst
+   input-types.rst
+   array_api.rst
+   verbose.rst
+   preview.rst
+   deprecation.rst
+   
+
+.. toctree::
+   :caption: Performance
+   :hidden:
+   :maxdepth: 2
+
+   guide/acceleration.rst
+
+.. toctree::
+   :caption: Learn 
+   :hidden:
+   :maxdepth: 2
+
+   Tutorials & Case Studies <tutorials.rst>
+   Medium Blogs <blogs.rst>
+
+.. toctree::
+   :caption: More
+   :hidden:
+   :maxdepth: 2
+   
+   Support <support.rst>
+   contribute.rst
+   license.rst
diff --git a/2025.0/_sources/input-types.rst.txt b/2025.0/_sources/input-types.rst.txt
new file mode 100644
index 0000000000..cd1714feb8
--- /dev/null
+++ b/2025.0/_sources/input-types.rst.txt
@@ -0,0 +1,43 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _input_types:
+
+=====================
+Supported input types
+=====================
+
+Just like Scikit-Learn estimators, estimators from Intel(R)'s Extension are able to accept
+and work with different classes of input data, including:
+
+- `Numpy <https://numpy.org/>`__ arrays.
+- Other array classes implementing the `Array API <https://data-apis.org/array-api/latest/>`__ protocol
+  (see :ref:`array_api` for details).
+- `SciPy sparse arrays and sparse matrices <https://docs.scipy.org/doc/scipy/tutorial/sparse.html>`__ (depending on the estimator).
+- `Pandas <https://pandas.pydata.org>`__ ``DataFrame`` and ``Series`` classes.
+
+In addition, Intel(R)'s Extension also supports:
+
+- `dpnp <https://github.com/IntelPython/dpnp>`__ arrays.
+- `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__ arrays.
+
+Stock Scikit-Learn estimators, depending on the version, might offer support for additional
+input types beyond this list, such as ``DataFrame`` and ``Series`` classes from other libraries
+like `Polars <https://pola.rs>`__.
+
+Intel(R)'s Extension currently does not offer accelerated routines for input types not listed
+here - when receiving an unsupported class, estimators will fall back to stock Scikit-Learn to
+handle it, so make sure to convert them to a supported type when using Intel(R)'s Extension.
diff --git a/2025.0/_sources/kaggle.rst.txt b/2025.0/_sources/kaggle.rst.txt
new file mode 100644
index 0000000000..364ab723d1
--- /dev/null
+++ b/2025.0/_sources/kaggle.rst.txt
@@ -0,0 +1,49 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _kaggle:
+
+####################
+Kaggle Kernels
+####################
+
+See Kaggle notebooks created by |intelex| developers.
+
+Acceleration
+---------------------------------
+
+`Introduction to scikit-learn-intelex <https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook>`_
+provides a summary of the speedup you can achieve with |intelex|.
+
+Machine Learning Workflows
+---------------------------------
+
+Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.
+
+Kaggle kernels that use scikit-learn and |intelex|:
+******************************************************************
+
+.. toctree::
+
+   Classification Tasks <kaggle/classification.rst>
+   Regression Tasks <kaggle/regression.rst>
+
+Kaggle kernels that use AutoML with |intelex|:
+******************************************************************
+
+.. toctree::
+
+   AutoML Workflows <kaggle/automl.rst>
diff --git a/2025.0/_sources/kaggle/automl.rst.txt b/2025.0/_sources/kaggle/automl.rst.txt
new file mode 100644
index 0000000000..0156e5ec89
--- /dev/null
+++ b/2025.0/_sources/kaggle/automl.rst.txt
@@ -0,0 +1,66 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |automl_with_intelex_jun| replace:: AutoML MultiClass Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with |intelex| 
+.. _automl_with_intelex_jun: https://www.kaggle.com/alex97andreev/tps-jun-autogluon-with-sklearnex
+
+.. |automl_with_intelex_tps_oct| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest) using AutoGluon with |intelex|
+.. _automl_with_intelex_tps_oct: https://www.kaggle.com/lordozvlad/fast-automl-with-intel-extension-for-scikit-learn/notebook
+
+.. |automl_with_intelex_tps_nov| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using EvalML and AutoGluon with |intelex|
+.. _automl_with_intelex_tps_nov: https://www.kaggle.com/lordozvlad/tps-nov-automl-with-intel-extension
+
+.. |automl_with_intelex_titanic| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with |intelex|
+.. _automl_with_intelex_titanic: https://www.kaggle.com/lordozvlad/titanic-automl-with-intel-extension-for-sklearn/notebook
+
+.. |automl_with_intelex_tps_jan| replace:: AutoML Binary Classification (Random Forest, SVR, Blending) using PyCaret with |intelex|
+.. _automl_with_intelex_tps_jan: https://www.kaggle.com/code/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex/notebook
+
+
+Kaggle Kernels that use AutoML and |intelex|
+--------------------------------------------
+
+The following Kaggle kernels show how to patch autoML frameworks with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 40 20
+
+   * - Kernel
+     - Goal
+   * - |automl_with_intelex_jun|_
+
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+   * - |automl_with_intelex_titanic|_
+
+       **Data:** Titanic datset
+     - Predict whether a passenger survivies
+   * - |automl_with_intelex_tps_oct|_
+
+       **Data:** [TPS Oct 2021] Synthetic molecular response data
+     - Predict the biological response of molecules given various chemical properties
+   * - |automl_with_intelex_tps_nov|_
+
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+     - Identify spam emails via features extracted from the email
+   * - |automl_with_intelex_tps_jan|_
+
+       **Data:** [TPS Jan 2022] Fictional Sales data
+     - Predict the corresponding item sales for each date-country-store-item combination
\ No newline at end of file
diff --git a/2025.0/_sources/kaggle/classification.rst.txt b/2025.0/_sources/kaggle/classification.rst.txt
new file mode 100644
index 0000000000..c0176cab62
--- /dev/null
+++ b/2025.0/_sources/kaggle/classification.rst.txt
@@ -0,0 +1,244 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Kaggle Kernels for Classification Tasks
+***************************************
+
+The following Kaggle kernels show how to patch scikit-learn with |intelex| for various classification tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+Binary Classification
++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Logistic Regression for Binary Classification
+       <https://www.kaggle.com/alexeykolobyanin/tps-nov-log-regression-with-sklearnex-17x-speedup>`_
+     
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+
+     - Identify spam emails via features extracted from the email
+     - 
+
+       - data preprocessing (normalization)
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Feature Importance in Random Forest for Binary Classification
+       <https://www.kaggle.com/code/lordozvlad/fast-feature-importance-using-scikit-learn-intelex/notebook>`_
+     
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+
+     - Identify spam emails via features extracted from the email
+     - 
+      
+       - reducing DataFrame memory usage
+       - computing feature importance with ELI5 and the default scikit-learn permutation importance
+       - training using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest for Binary Classification
+       <https://www.kaggle.com/andreyrus/tps-apr-rf-with-intel-extension-for-scikit-learn>`_
+     
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+      
+       - data preprocessing
+       - feature construction
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for Binary Classification
+       <https://www.kaggle.com/napetrov/tps04-svm-with-intel-extension-for-scikit-learn>`_
+       
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+     
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) with Feature Preprocessing for Binary Classification
+       <https://www.kaggle.com/napetrov/tps04-svm-with-scikit-learn-intelex>`_
+      
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+     
+       - data preprocessing
+       - feature engineering
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+MultiClass Classification
++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Logistic Regression for MultiClass Classification with Quantile Transformer
+       <https://www.kaggle.com/kppetrov/tps-jun-fast-logreg-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+
+       - data preprocessing with Quantile Transformer
+       - training and prediction using scikit-learn-intelex
+       - search for optimal paramters using Optuna
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for MultiClass Classification (example 1)
+       <https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS May 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+
+   * - `Stacking Classifer with Logistic Regression, kNN, Random Forest, and Quantile Transformer
+       <https://www.kaggle.com/owerbat/tps-jun-fast-stacking-with-scikit-learn-intelex>`_
+      
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+
+       - data preprocessing: one-hot encoding, dimensionality reduction with PCA, normalization
+       - creating a stacking classifier with logistic regression, kNN, and random forest,
+         and a pipeline of Quantile Transformer and another logistic regression as a final estimator
+       - searching for optimal parameters for the stacking classifier
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for MultiClass Classification (example 2)
+       <https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup>`_
+       
+       **Data:** [TPS Dec 2021] Synthetic Forest Cover Type data
+     - Predict the forest cover type
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Feature Importance in Random Forest for MultiClass Classification
+       <https://www.kaggle.com/code/lordozvlad/tps-dec-fast-feature-importance-with-sklearnex>`_
+     
+       **Data:** [TPS Dec 2021] Synthetic Forest Cover Type data
+
+     - Predict the forest cover type
+     - 
+      
+       - reducing DataFrame memory usage
+       - computing feature importance with ELI5
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `k-Nearest Neighbors (kNN) for MultiClass Classification
+       <https://www.kaggle.com/code/alexeykolobyanin/tps-feb-knn-with-sklearnex-13x-speedup>`_
+       
+       **Data:** [TPS Feb 2022] Bacteria DNA
+     - Predict bacteria species based on repeated lossy measurements of DNA snippets
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Classification Tasks in Computer Vision
++++++++++++++++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Support Vector Classification (SVC) for MultiClass Classification (CV task)
+       <https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-mnist?scriptVersionId=58739300>`_
+       
+       **Data:** Digit Recognizer (MNIST)
+     - Recognize hand-written digits
+     - 
+       
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `k-Nearest Neighbors (kNN) for MultiClass Classification (CV task)
+       <https://www.kaggle.com/kppetrov/fast-knn-using-scikit-learn-intelex-for-mnist?scriptVersionId=58738635>`_
+       
+       **Data:** Digit Recognizer (MNIST)
+     - Recognize hand-written digits
+     - 
+     
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Classification Tasks in Natural Language Processing
++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Support Vector Classification (SVC) for a Binary Classification (NLP task)
+       <https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-nlp?scriptVersionId=58739339>`_
+       
+       **Data:** Natural Language Processing with Disaster Tweets
+     - Predict which tweets are about real disasters and which ones are not
+     - 
+     
+       - data preprocessing
+       - TF-IDF calculation
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `One-vs-Rest Support Vector Machine (SVM) with Text Data for MultiClass Classification
+       <https://www.kaggle.com/kppetrov/using-scikit-learn-intelex-for-what-s-cooking?scriptVersionId=58739642>`_
+      
+       **Data:** What's Cooking
+     - Use recipe ingredients to predict the cuisine
+     - 
+     
+       - feature extraction using TfidfVectorizer
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for Binary Classification with Sparse Data (NLP task)
+       <https://www.kaggle.com/code/alex97andreev/fast-svm-for-sparse-data-from-nlp-problem>`_
+       
+       **Data:** Stack Overflow questions
+     - Predict the binary quality rating for Stack Overflow questions
+     - 
+     
+       - data preprocessing
+       - TF-IDF calculation
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
diff --git a/2025.0/_sources/kaggle/regression.rst.txt b/2025.0/_sources/kaggle/regression.rst.txt
new file mode 100644
index 0000000000..abdbebc15b
--- /dev/null
+++ b/2025.0/_sources/kaggle/regression.rst.txt
@@ -0,0 +1,156 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Kaggle Kernels for Regression Tasks
+************************************
+
+The following Kaggle kernels show how to patch scikit-learn with |intelex| for various regression tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+Using a Single Regressor
+++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel
+       <https://www.kaggle.com/kppetrov/tps-jul-2021-baseline-with-nusvr-model/notebook>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - data preprocessing
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+   * - `Nu Support Vector Regression (nuSVR)
+       <https://www.kaggle.com/alexeykolobyanin/tps-aug-nusvr-with-intel-extension-for-sklearn>`__
+     
+       **Data:** [TPS Aug 2021] Synthetic loan data
+     - Calculate loss associated with a loan defaults
+     -
+
+       - data preprocessing
+       - feature engineering
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Nu Support Vector Regression (nuSVR)
+       <https://www.kaggle.com/alexeykolobyanin/house-prices-nusvr-sklearn-intelex-4x-speedup>`__
+       
+       **Data:** House Prices dataset
+     - Predict sale prices for a property based on its characteristics
+     -
+
+       - data preprocessing
+       - exploring outliers
+       - feature engineering
+       - filling missing values
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest Regression
+       <https://www.kaggle.com/pahandrovich/tps-jul-2021-fast-randomforest-with-sklearnex>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - checking correlation between features
+       - search for best paramters using GridSearchCV
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Random Forest Regression with Feature Engineering
+       <https://www.kaggle.com/alexeykolobyanin/tps-jul-rf-with-intel-extension-for-scikit-learn>`_
+     
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - data preprocessing
+       - feature engineering
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest Regression with Feature Importance Computation
+       <https://www.kaggle.com/code/lordozvlad/tps-mar-fast-workflow-using-scikit-learn-intelex>`_
+
+       **Data:** [TPS Mar 2022] Spatio-temporal traffic data
+     - Forecast twelve-hours of traffic flow in a major U.S. metropolitan area
+     -
+
+       - feature engineering
+       - computing feature importance with ELI5
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Ridge Regression
+       <https://www.kaggle.com/alexeykolobyanin/tps-sep-ridge-with-sklearn-intelex-2x-speedup>`_
+     
+     
+       **Data:** [TPS Sep 2021] Synthetic insurance data
+     - Predict the probability of a customer making a claim upon an insurance policy
+     -
+
+       - data preprocessing
+       - filling missing values
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Stacking Regressors
++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Stacking Regressor with Random Fores, SVR, and LASSO
+       <https://www.kaggle.com/alexeykolobyanin/tps-jul-stacking-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - feature engineering
+       - creating a stacking regressor
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data
+       <https://www.kaggle.com/alexeykolobyanin/predict-sales-stacking-with-scikit-learn-intelex>`_
+       
+       **Data:** Predict Future Sales dataset
+     - Predict total sales for every product and store in the next month based on daily sales data
+     -
+
+       - data preprocessing
+       - creating a stacking regressor
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
diff --git a/latest/_sources/releases.rst.txt b/2025.0/_sources/license.rst.txt
similarity index 73%
rename from latest/_sources/releases.rst.txt
rename to 2025.0/_sources/license.rst.txt
index 186dc7cb15..df68abaea6 100644
--- a/latest/_sources/releases.rst.txt
+++ b/2025.0/_sources/license.rst.txt
@@ -14,10 +14,10 @@
 .. * limitations under the License.
 .. *******************************************************************************/
 
-.. _Releases:
+.. _license:
 
 ############################################
-Release Notes
+License
 ############################################
 
-The updates of Intel® Extension for Scikit-learn* for each release can be found in `Releases <https://github.com/intel/scikit-learn-intelex/releases>`_.                                                                                                                                    
\ No newline at end of file
+License: https://github.com/intel/scikit-learn-intelex/blob/main/LICENSE
\ No newline at end of file
diff --git a/2025.0/_sources/non-scikit-algorithms.rst.txt b/2025.0/_sources/non-scikit-algorithms.rst.txt
new file mode 100644
index 0000000000..620461843f
--- /dev/null
+++ b/2025.0/_sources/non-scikit-algorithms.rst.txt
@@ -0,0 +1,44 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Non-Scikit-Learn Algorithms
+===========================
+Algorithms not presented in the original scikit-learn are described here. All algorithms are 
+available for both CPU and GPU (including distributed mode)
+
+BasicStatistics
+---------------
+.. autoclass:: sklearnex.basic_statistics.BasicStatistics
+.. automethod:: sklearnex.basic_statistics.BasicStatistics.fit
+
+IncrementalBasicStatistics
+--------------------------
+.. autoclass:: sklearnex.basic_statistics.IncrementalBasicStatistics
+.. automethod:: sklearnex.basic_statistics.IncrementalBasicStatistics.fit
+.. automethod:: sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit
+
+IncrementalEmpiricalCovariance
+------------------------------
+.. autoclass:: sklearnex.covariance.IncrementalEmpiricalCovariance
+.. automethod:: sklearnex.covariance.IncrementalEmpiricalCovariance.fit
+.. automethod:: sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit
+
+IncrementalLinearRegression
+---------------------------
+.. autoclass:: sklearnex.linear_model.IncrementalLinearRegression
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.fit
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.partial_fit
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.predict
\ No newline at end of file
diff --git a/2025.0/_sources/oneapi-gpu.rst.txt b/2025.0/_sources/oneapi-gpu.rst.txt
new file mode 100644
index 0000000000..37bfbf3a92
--- /dev/null
+++ b/2025.0/_sources/oneapi-gpu.rst.txt
@@ -0,0 +1,114 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _oneapi_gpu:
+
+##############################################################
+oneAPI and GPU support in |intelex|
+##############################################################
+
+|intelex| supports oneAPI concepts, which
+means that algorithms can be executed on different devices: CPUs and GPUs.
+This is done via integration with
+`dpctl <https://intelpython.github.io/dpctl/latest/index.html>`_ package that
+implements core oneAPI concepts like queues and devices.
+
+Prerequisites
+-------------
+
+For execution on GPU, DPC++ compiler runtime and driver are required. Refer to `DPC++ system
+requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_ for details.
+
+DPC++ compiler runtime can be installed either from PyPI or Anaconda:
+
+- Install from PyPI::
+
+     pip install dpcpp-cpp-rt
+
+- Install using Conda via the Intel repository::
+
+     conda install dpcpp_cpp_rt -c https://software.repos.intel.com/python/conda/
+
+Device offloading
+-----------------
+
+|intelex| offers two options for running an algorithm on a
+specific device with the help of dpctl:
+
+- Pass input data as `dpctl.tensor.usm_ndarray <https://intelpython.github.io/dpctl/latest/docfiles/dpctl/usm_ndarray.html#dpctl.tensor.usm_ndarray>`_ to the algorithm.
+
+  The computation will run on the device where the input data is
+  located, and the result will be returned as :code:`usm_ndarray` to the same
+  device.
+
+  .. note::
+    All the input data for an algorithm must reside on the same device.
+
+  .. warning::
+    The :code:`usm_ndarray` can only be consumed by the base methods
+    like :code:`fit`, :code:`predict`, and :code:`transform`.
+    Note that only the algorithms in |intelex| support
+    :code:`usm_ndarray`. The algorithms from the stock version of scikit-learn
+    do not support this feature.
+- Use global configurations of |intelex|\*:
+  
+  1. The :code:`target_offload` option can be used to set the device primarily
+     used to perform computations. Accepted data types are :code:`str` and
+     :code:`dpctl.SyclQueue`. If you pass a string to :code:`target_offload`,
+     it should either be ``"auto"``, which means that the execution
+     context is deduced from the location of input data, or a string
+     with SYCL* filter selector. The default value is ``"auto"``.
+  
+  2. The :code:`allow_fallback_to_host` option
+     is a Boolean flag. If set to :code:`True`, the computation is allowed 
+     to fallback to the host device when a particular estimator does not support
+     the selected device. The default value is :code:`False`.
+
+These options can be set using :code:`sklearnex.set_config()` function or
+:code:`sklearnex.config_context`. To obtain the current values of these options,
+call :code:`sklearnex.get_config()`.
+
+.. note::
+     Functions :code:`set_config`, :code:`get_config` and :code:`config_context`
+     are always patched after the :code:`sklearnex.patch_sklearn()` call.
+
+.. rubric:: Compatibility considerations
+
+For compatibility reasons, algorithms in |intelex| may be offloaded to the device using
+:code:`daal4py.oneapi.sycl_context`. However, it is recommended to use one of the options
+described above for device offloading instead of using :code:`sycl_context`.
+
+Example
+-------
+
+An example on how to patch your code with Intel CPU/GPU optimizations:
+
+.. code-block:: python
+
+   from sklearnex import patch_sklearn, config_context
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   with config_context(target_offload="gpu:0"):
+      clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+
+.. note:: Current offloading behavior restricts fitting and inference of any models to be
+     in the same context or absence of context. For example, a model trained in the GPU context with
+     target_offload="gpu:0" throws an error if the inference is made outside the same GPU context.
diff --git a/2025.0/_sources/preview.rst.txt b/2025.0/_sources/preview.rst.txt
new file mode 100644
index 0000000000..7f19c6a264
--- /dev/null
+++ b/2025.0/_sources/preview.rst.txt
@@ -0,0 +1,73 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _preview:
+
+#####################
+Preview Functionality
+#####################
+
+Some of implemented functionality in |intelex| doesn't meet one or few of next requirements
+for being enabled by default for all users:
+
+* The functionality API is not stable and can be changed in future
+* The functionality doesn't have full compatibility with stock Scikit-learn
+* The functionality misses performance targets compared to stock Scikit-learn or previously available version of functionality
+* The functionality is not fully tested
+
+This type of functionality is available under **preview mode** of |intelex| and located in
+the corresponding module (`sklearnex.preview`).
+
+Preview functionality *may* or *may not* participate in patching of Scikit-learn.
+For example, a preview estimator may be a replacement for a stock one or a completely new one.
+
+To enable preview functionality, you need to set the `SKLEARNEX_PREVIEW` environment variable
+to non-empty value before patching of Scikit-learn.
+For example, you can set the environment variable in the following way:
+
+- On Linux* OS ::
+
+     export SKLEARNEX_PREVIEW=1
+
+- On Windows* OS ::
+
+     set SKLEARNEX_PREVIEW=1
+
+Then, you can import Scikit-learn estimator patched with a preview one from `sklearnex.preview` module::
+
+     from sklearnex import patch_sklearn
+     patch_sklearn()
+     from sklearn.decomposition import IncrementalPCA
+     print(IncrementalPCA.__module__)
+     # output:
+     # sklearnex.preview.decomposition.incremental_pca
+
+Current list of preview estimators:
+
+.. list-table::
+   :widths: 30 20 10
+   :header-rows: 1
+   :align: left
+
+   * - Estimator name
+     - Module
+     - Is patching supported
+   * - EmpiricalCovariance
+     - sklearnex.preview.covariance
+     - Yes
+   * - IncrementalPCA
+     - sklearnex.preview.decomposition
+     - Yes
diff --git a/2025.0/_sources/quick-start.rst.txt b/2025.0/_sources/quick-start.rst.txt
new file mode 100644
index 0000000000..bc6ac8798a
--- /dev/null
+++ b/2025.0/_sources/quick-start.rst.txt
@@ -0,0 +1,405 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |intelex_repo| replace:: |intelex| repository
+.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
+
+####################
+Quick Start
+####################
+
+Get ready to elevate your scikit-learn code with |intelex| and experience the benefits of accelerated performance in just a few simple steps. 
+
+Compatibility with Scikit-learn*
+---------------------------------
+
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.
+
+Integrate |intelex|
+--------------------
+
+Patching 
+**********************
+
+Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension. 
+This action is called ``patching``. This is not a permanent change so you can always undo the patching if necessary.
+
+To patch Intel® Extension for Scikit-learn, use one of these methods: 
+
+.. list-table:: 
+   :header-rows: 1
+   :align: left
+
+   * - Method
+     - Action
+   * - Use a flag in the command line
+     - Run this command:
+
+       :: 
+         
+          python -m sklearnex my_application.py
+   * - Modify your script 
+     - Add the following lines:
+
+       ::
+ 
+          from sklearnex import patch_sklearn
+          patch_sklearn()   
+   * - Import an estimator from the ``sklearnex`` module
+     - Run this command:
+
+       ::
+
+          from sklearnex.neighbors import NearestNeighbors
+
+
+
+These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.
+
+   
+**Example**
+
+This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.
+  
+.. code-block:: python
+  :caption: Example: Drop-In Patching
+   
+    import numpy as np
+    from sklearnex import patch_sklearn
+    patch_sklearn()
+
+    # You need to re-import scikit-learn algorithms after the patch
+    from sklearn.cluster import KMeans
+  
+    # The use of the original Scikit-learn is not changed
+    X = np.array([[1,  2], [1,  4], [1,  0],
+                [10, 2], [10, 4], [10, 0]])
+    kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
+    print(f"kmeans.labels_ = {kmeans.labels_}")
+
+
+Global Patching
+**********************
+
+You can also use global patching to patch all your scikit-learn applications without any additional actions.
+
+Before you begin, make sure that you have read and write permissions for Scikit-learn files. 
+
+With global patching, you can:
+
+.. list-table:: 
+   :header-rows: 1
+   :align: left
+
+   * - Task
+     - Action
+     - Note
+   * - Patch all supported algorithms
+     - Run this command:
+
+       :: 
+         
+          python -m sklearnex.glob patch_sklearn
+     
+     - If you run the global patching command several times with different parameters, then only the last configuration is applied.
+   * - Patch selected algorithms
+     - Use ``--algorithm`` or ``-a`` keys with a list of algorithms to patch. For example, to patch only ``SVC`` and ``RandomForestClassifier`` estimators, run
+
+       ::
+ 
+           python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier
+  
+     -   
+   * - Enable global patching via code
+     - Use the ``patch_sklearn`` function with the ``global_patch`` argument:
+
+       ::
+
+          from sklearnex import patch_sklearn
+          patch_sklearn(global_patch=True)
+          import sklearn
+      
+     - After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.
+   * - Disable patching notifications
+     - Use ``--no-verbose`` or ``-nv`` keys:
+
+       ::
+
+          python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier -nv
+     -  
+   * - Disable global patching
+     - Run this command:
+
+       ::
+
+          python -m sklearnex.glob unpatch_sklearn
+     -
+   * - Disable global patching via code
+     - Use the ``global_patch`` argument in the ``unpatch_sklearn`` function
+
+       ::
+
+          from sklearnex import unpatch_sklearn
+          unpatch_sklearn(global_patch=True)
+     -
+    
+.. tip:: If you clone an environment with enabled global patching, it will already be applied in the new environment.
+
+Unpatching
+**********************
+
+To undo the patch (also called `unpatching`) is to return scikit-learn to original implementation and
+replace patched algorithms with the stock scikit-learn algorithms.
+
+To unpatch successfully, you must reimport the scikit-learn package::
+
+  sklearnex.unpatch_sklearn()
+  # Re-import scikit-learn algorithms after the unpatch
+  from sklearn.cluster import KMeans  
+
+
+Installation 
+--------------------
+
+.. contents:: :local:
+
+.. tip:: To prevent version conflicts, we recommend creating and activating a new environment for |intelex|. 
+
+Install from PyPI 
+**********************
+
+Recommended by default. 
+
+To install |intelex|, run:
+
+::
+
+  pip install scikit-learn-intelex
+
+**Supported Configurations**
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+
+   * - OS / Python version
+     - Python 3.9
+     - Python 3.10
+     - Python 3.11
+     - Python 3.12
+   * - Linux* OS
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+   * - Windows* OS
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+
+
+
+Install from Anaconda* Cloud
+********************************************
+
+To prevent version conflicts, we recommend installing `scikit-learn-intelex` into a new conda environment.
+
+.. tabs::
+
+   .. tab:: Conda-Forge channel
+
+      Recommended by default. 
+      
+      To install, run::
+
+        conda install scikit-learn-intelex -c conda-forge
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+         * - Windows* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+
+
+   .. tab:: Intel channel
+
+      Recommended for the Intel® Distribution for Python users. 
+
+      To install, run::
+
+        conda install scikit-learn-intelex -c https://software.repos.intel.com/python/conda/
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+         * - Windows* OS
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+ 
+
+
+   .. tab:: Main channel
+
+      To install, run::
+
+        conda install scikit-learn-intelex
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+         * - Windows* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+
+
+
+Build from Sources
+**********************
+
+See `Installation instructions <https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md>`_ to build |intelex| from the sources.
+
+Install Intel*(R) AI Tools
+****************************
+
+Download the Intel AI Tools `here <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html>`_. The extension is already included.
+
+Release Notes
+-------------------
+
+See the `Release Notes <https://github.com/intel/scikit-learn-intelex/releases>`_ for each version of Intel® Extension for Scikit-learn*.  
+
+System Requirements 
+--------------------
+
+Hardware Requirements
+**********************
+
+.. tabs::
+
+   .. tab:: CPU
+
+      All processors with ``x86`` architecture with at least one of the following instruction sets:
+
+        - SSE2
+        - SSE4.2
+        - AVX2
+        - AVX512
+       
+      .. note:: ARM* architecture is not supported.
+
+   .. tab:: GPU
+
+      - All Intel® integrated and discrete GPUs
+      - Intel® GPU drivers
+
+
+.. tip:: Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our :ref:`blogs <blogs>`.
+
+
+Software Requirements
+**********************
+
+.. tabs::
+
+   .. tab:: CPU
+
+      - Linux* OS: Ubuntu* 18.04 or newer
+      - Windows* OS 10 or newer
+      - Windows* Server 2019 or newer
+
+   .. tab:: GPU
+
+      - Linux* OS: Ubuntu* 18.04 or newer
+      - Windows* OS 10 or newer
+      - Windows* Server 2019 or newer
+      
+      .. important::
+         
+         If you use accelerators, refer to `oneAPI DPC++/C++ Compiler System Requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_.
+
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:
+
+* 1.0.X
+* 1.1.X
+* 1.2.X 
+* 1.3.X
+
+Memory Requirements
+**********************
+By default, algorithms in |intelex| run in the multi-thread mode. This mode uses all available threads. 
+Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Single-thread mode
+     - Multi-thread mode
+   * - SVM
+     - Both Scikit-learn and |intelex| consume approximately the same amount of RAM.
+     - In |intelex|, an algorithm with ``N`` threads consumes ``N`` times more RAM.
+
+In all |intelex| algorithms with GPU support, computations run on device memory. 
+The device memory must be large enough to store a copy of the entire dataset.
+You may also require additional device memory for internal arrays that are used in computation.
+
+
+.. seealso::
+
+   :ref:`Samples<samples>`
diff --git a/2025.0/_sources/samples.rst.txt b/2025.0/_sources/samples.rst.txt
new file mode 100644
index 0000000000..4ca25f6d92
--- /dev/null
+++ b/2025.0/_sources/samples.rst.txt
@@ -0,0 +1,75 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _samples:
+
+#######
+Samples 
+#######
+
+The following samples are also provided as Jupyter notebooks in |intelex| repository.
+If you want to run them locally, refer to `these instructions
+<https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks>`_.
+
+.. rubric:: Classification Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   k-Nearest Neighbors (kNN) for MNIST dataset <samples/knn_mnist.ipynb>
+   Logistic Regression for Cifar dataset <samples/logistictic_regression_cifar.ipynb>
+   Support Vector Classification (SVC) for Adult dataset <samples/svc_adult.ipynb>
+
+.. rubric:: Regression Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   ElasticNet for Airlines DepDelay dataset <samples/ElasticNet.ipynb>
+   Lasso Regression for YearPredictionMSD dataset <samples/lasso_regression.ipynb>
+   Linear Regression for YearPredictionMSD dataset <samples/linear_regression.ipynb>
+   Nu-Support Vector Regression (NuSVR) for Medical Charges dataset <samples/nusvr_medical_charges.ipynb>
+   Random Forest for Yolanda dataset <samples/random_forest_yolanda.ipynb>
+   Rigde Regression for Airlines DepDelay dataset <samples/ridge_regression.ipynb>
+
+.. rubric:: Clustering Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   Kmeans for spoken arabic digit dataset <samples/kmeans.ipynb>
+   DBSCAN for spoken arabic digit dataset <samples/dbscan.ipynb>
+
+.. rubric:: Dimensionality Reduction Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   Intel® Extension for Scikit-learn TSNE example <samples/tsne.ipynb>
+
+.. rubric:: Jupyter Notebook Samples
+
+.. toctree::
+   :maxdepth: 1
+
+   Intel® Extension for Scikit-Learn* Getting Started Sample <https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted>
+   Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample <https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult>
+
+.. seealso::
+
+   There are also :ref:`Kaggle kernels <kaggle>` that use |intelex| for a variety of machine learning scenarios.
+
+.. note:: Explore the complete list of oneAPI code samples in the `oneAPI Samples Catalog <https://oneapi-src.github.io/oneAPI-samples/>`_. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.
diff --git a/2025.0/_sources/samples/ElasticNet.ipynb.txt b/2025.0/_sources/samples/ElasticNet.ipynb.txt
new file mode 100644
index 0000000000..29e5dca851
--- /dev/null
+++ b/2025.0/_sources/samples/ElasticNet.ipynb.txt
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/daal4py_data_science.ipynb.txt b/2025.0/_sources/samples/daal4py_data_science.ipynb.txt
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2025.0/_sources/samples/daal4py_data_science.ipynb.txt
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2025.0/_sources/samples/dbscan.ipynb.txt b/2025.0/_sources/samples/dbscan.ipynb.txt
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2025.0/_sources/samples/dbscan.ipynb.txt
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/kmeans.ipynb.txt b/2025.0/_sources/samples/kmeans.ipynb.txt
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2025.0/_sources/samples/kmeans.ipynb.txt
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/knn_mnist.ipynb.txt b/2025.0/_sources/samples/knn_mnist.ipynb.txt
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2025.0/_sources/samples/knn_mnist.ipynb.txt
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/lasso_regression.ipynb.txt b/2025.0/_sources/samples/lasso_regression.ipynb.txt
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2025.0/_sources/samples/lasso_regression.ipynb.txt
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/linear_regression.ipynb.txt b/2025.0/_sources/samples/linear_regression.ipynb.txt
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2025.0/_sources/samples/linear_regression.ipynb.txt
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/logistictic_regression_cifar.ipynb.txt b/2025.0/_sources/samples/logistictic_regression_cifar.ipynb.txt
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2025.0/_sources/samples/logistictic_regression_cifar.ipynb.txt
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/nusvr_medical_charges.ipynb.txt b/2025.0/_sources/samples/nusvr_medical_charges.ipynb.txt
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2025.0/_sources/samples/nusvr_medical_charges.ipynb.txt
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/random_forest_yolanda.ipynb.txt b/2025.0/_sources/samples/random_forest_yolanda.ipynb.txt
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2025.0/_sources/samples/random_forest_yolanda.ipynb.txt
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/ridge_regression.ipynb.txt b/2025.0/_sources/samples/ridge_regression.ipynb.txt
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2025.0/_sources/samples/ridge_regression.ipynb.txt
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/svc_adult.ipynb.txt b/2025.0/_sources/samples/svc_adult.ipynb.txt
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2025.0/_sources/samples/svc_adult.ipynb.txt
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/samples/tsne.ipynb.txt b/2025.0/_sources/samples/tsne.ipynb.txt
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2025.0/_sources/samples/tsne.ipynb.txt
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/_sources/support.rst.txt b/2025.0/_sources/support.rst.txt
new file mode 100644
index 0000000000..03a2dddadb
--- /dev/null
+++ b/2025.0/_sources/support.rst.txt
@@ -0,0 +1,34 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+######################################################
+Intel(R) Extension for Scikit-learn Support
+######################################################
+
+
+We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn. 
+
+Use the following methods if you face any challenges. 
+
+
+Issues
+----------------------------------
+
+If you have a problem, check out the `GitHub Issues <https://github.com/intel/scikit-learn-intelex/issues>`_ to see if the issue you want to address is already reported. 
+
+You may find users that have encountered the same bug or have similar ideas for changes or updates.
+
+You can use issues to report a problem, make a feature request, or add comments on an existing issue.
diff --git a/2025.0/_sources/tutorials.rst.txt b/2025.0/_sources/tutorials.rst.txt
new file mode 100644
index 0000000000..ce778bb5ab
--- /dev/null
+++ b/2025.0/_sources/tutorials.rst.txt
@@ -0,0 +1,92 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+
+################################################
+|intelex| Tutorials & Case Studies
+################################################
+
+Explore the tutorials & case studies to help you to harness the full potential of |intelex|.
+
+Tutorials
+----------------------------------
+
+.. grid:: 3
+
+   .. grid-item-card:: Introduction to scikit-learn* Essentials for Machine Learning
+      :link: https://www.intel.com/content/www/us/en/developer/videos/introduction-to-scikit-learn-essentials-for-ml.html
+      :padding: 1
+
+      An overview of scikit-learn essentials.
+
+   .. grid-item-card:: Advanced scikit-learn* Essentials for Machine Learning
+      :link: https://www.intel.com/content/www/us/en/developer/videos/advanced-scikit-learn-essentials-for-ml.html
+      :padding: 1
+      
+      Special technique to perform scikit-learn computation on Intel GPUs. 
+   
+   .. grid-item-card:: Develop Efficient AI Solutions with Accelerated Machine Learning
+      :link: https://www.intel.com/content/www/us/en/developer/videos/accelerated-machine-learning-for-ai-solutions.html
+      :padding: 1
+
+      Techniques for maximizing Intel® Extension for Scikit-learn*. 
+
+   .. grid-item-card:: Getting started with classical Machine Learning Frameworks using Google Colaboratory
+      :link: https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Getting-started-with-classical-Machine-Learning-Frameworks-using/post/1450139
+      :padding: 1
+
+      Simple Installation of Intel® Extension for Scikit-learn* on Google Colaboratory.     
+
+   .. grid-item-card:: Accelerate Machine Learning Workloads: K-means and GPairs Algorithms
+      :link: https://www.intel.com/content/www/us/en/developer/videos/accelerate-ml-workloads-k-means-gpairs-algorithms.html
+      :padding: 1
+
+      An introduction to Intel® Extension for Scikit-learn*.
+
+   .. grid-item-card:: Benchmarking Intel® Extension for Scikit-learn*: How Much Faster Is It?
+      :link: https://www.intel.com/content/www/us/en/developer/articles/technical/benchmarking-intel-extension-for-scikit-learn.html
+      :padding: 1
+
+      Explore and compare the performance of the Intel Extension for Scikit-learn* vs. stock Scikit-learn*.
+
+   .. grid-item-card:: Drive Innovation & Performance into Your scikit-learn* Machine Learning Tasks
+      :link: https://www.intel.com/content/www/us/en/developer/videos/drive-innovation-performance-scikit-learn-ml-tasks.html
+      :padding: 1
+
+      An overview of scikit-learn essentials.
+
+Case Studies 
+----------------------------------
+
+.. grid:: 3
+
+   .. grid-item-card:: Greener Machine Learning Computing with Intel® AI Acceleration
+      :link: https://www.intel.com/content/www/us/en/developer/articles/case-study/greener-machine-learning-with-intel-ai.html
+      :padding: 1
+
+      Lowering the Environmental Impact of AI Workloads by 7x using Intel® Extension for Scikit-learn*.
+
+   .. grid-item-card:: Supply Chain Optimization at Enterprise Scale
+      :link: https://www.intel.com/content/www/us/en/developer/articles/technical/supply-chain-optimization-at-enterprise-scale.html
+      :padding: 1
+
+      Leveraging Open-Source AI Technologies with Red Hat OpenShift* Data Science and Intel® Architecture.
+
+   .. grid-item-card:: HippoScreen Improves AI Performance by 2.4x with oneAPI Tools
+      :link: https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html
+      :padding: 1
+
+      The neurotechnology startup used |intelex| to the improve efficiency and build times of deep-learning models used in its Brain Waves AI system.
\ No newline at end of file
diff --git a/2025.0/_sources/verbose.rst.txt b/2025.0/_sources/verbose.rst.txt
new file mode 100644
index 0000000000..cf77cc0669
--- /dev/null
+++ b/2025.0/_sources/verbose.rst.txt
@@ -0,0 +1,54 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _verbose:
+
+############
+Verbose Mode
+############
+
+Use |intelex| in verbose mode to find out which implementation of the algorithm is currently used,
+|intelex| or original Scikit-learn.
+
+.. note:: Verbose mode is only available for :ref:`supported algorithms <sklearn_algorithms>`.
+
+To enable verbose mode, set the ``SKLEARNEX_VERBOSE`` environment variable as shown below:
+
+- On Linux* OS ::
+
+     export SKLEARNEX_VERBOSE=INFO
+
+- On Windows* OS ::
+
+     set SKLEARNEX_VERBOSE=INFO
+
+Alternatively, get |intelex| logger and set its logging level in the Python code::
+
+     import logging
+     logger = logging.getLogger('sklearnex')
+     logger.setLevel(logging.INFO)
+
+During the calls that use Intel-optimized scikit-learn, you will receive additional print statements
+that indicate which implementation is being called.
+These print statements are only available for :ref:`supported algorithms <sklearn_algorithms>`.
+
+For example, for DBSCAN you get one of these print statements depending on which implementation is used::
+
+    INFO:sklearnex: sklearn.cluster.DBSCAN.fit: running accelerated version on CPU
+
+::
+
+    INFO:sklearnex: sklearn.cluster.DBSCAN.fit: fallback to original Scikit-learn
diff --git a/2025.0/_sphinx_design_static/design-tabs.js b/2025.0/_sphinx_design_static/design-tabs.js
new file mode 100644
index 0000000000..b25bd6a4fa
--- /dev/null
+++ b/2025.0/_sphinx_design_static/design-tabs.js
@@ -0,0 +1,101 @@
+// @ts-check
+
+// Extra JS capability for selected tabs to be synced
+// The selection is stored in local storage so that it persists across page loads.
+
+/**
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+ */
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+const storageKeyPrefix = "sphinx-design-tab-id-";
+
+/**
+ * Create a key for a tab element.
+ * @param {HTMLElement} el - The tab element.
+ * @returns {[string, string, string] | null} - The key.
+ *
+ */
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+  let syncGroup = el.getAttribute("data-sync-group");
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+  return [syncGroup, syncId, syncGroup + "--" + syncId];
+}
+
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+ * Initialize the tab selection.
+ *
+ */
+function ready() {
+  // Find all tabs with sync data
+
+  /** @type {string[]} */
+  let groups = [];
+
+  document.querySelectorAll(".sd-tab-label").forEach((label) => {
+    if (label instanceof HTMLElement) {
+      let data = create_key(label);
+      if (data) {
+        let [group, id, key] = data;
+
+        // add click event listener
+        // @ts-ignore
+        label.onclick = onSDLabelClick;
+
+        // store map of key to elements
+        if (!sd_id_to_elements[key]) {
+          sd_id_to_elements[key] = [];
+        }
+        sd_id_to_elements[key].push(label);
+
+        if (groups.indexOf(group) === -1) {
+          groups.push(group);
+          // Check if a specific tab has been selected via URL parameter
+          const tabParam = new URLSearchParams(window.location.search).get(
+            group
+          );
+          if (tabParam) {
+            console.log(
+              "sphinx-design: Selecting tab id for group '" +
+                group +
+                "' from URL parameter: " +
+                tabParam
+            );
+            window.sessionStorage.setItem(storageKeyPrefix + group, tabParam);
+          }
+        }
+
+        // Check is a specific tab has been selected previously
+        let previousId = window.sessionStorage.getItem(
+          storageKeyPrefix + group
+        );
+        if (previousId === id) {
+          // console.log(
+          //   "sphinx-design: Selecting tab from session storage: " + id
+          // );
+          // @ts-ignore
+          label.previousElementSibling.checked = true;
+        }
+      }
+    }
+  });
+}
+
+/**
+ *  Activate other tabs with the same sync id.
+ *
+ * @this {HTMLElement} - The element that was clicked.
+ */
+function onSDLabelClick() {
+  let data = create_key(this);
+  if (!data) return;
+  let [group, id, key] = data;
+  for (const label of sd_id_to_elements[key]) {
+    if (label === this) continue;
+    // @ts-ignore
+    label.previousElementSibling.checked = true;
+  }
+  window.sessionStorage.setItem(storageKeyPrefix + group, id);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/2025.0/_sphinx_design_static/sphinx-design.min.css b/2025.0/_sphinx_design_static/sphinx-design.min.css
new file mode 100644
index 0000000000..860c36da0f
--- /dev/null
+++ b/2025.0/_sphinx_design_static/sphinx-design.min.css
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#d3d4d5;--sd-color-muted-highlight: #5c636a;--sd-color-dark-highlight: #1c1f23;--sd-color-black-highlight: black;--sd-color-white-highlight: #d9d9d9;--sd-color-primary-bg: rgba(0, 113, 188, 0.2);--sd-color-secondary-bg: rgba(108, 117, 125, 0.2);--sd-color-success-bg: rgba(40, 167, 69, 0.2);--sd-color-info-bg: rgba(23, 162, 184, 0.2);--sd-color-warning-bg: rgba(240, 179, 126, 0.2);--sd-color-danger-bg: rgba(220, 53, 69, 0.2);--sd-color-light-bg: rgba(248, 249, 250, 0.2);--sd-color-muted-bg: rgba(108, 117, 125, 0.2);--sd-color-dark-bg: rgba(33, 37, 41, 0.2);--sd-color-black-bg: rgba(0, 0, 0, 0.2);--sd-color-white-bg: rgba(255, 255, 255, 0.2);--sd-color-primary-text: #fff;--sd-color-secondary-text: #fff;--sd-color-success-text: #fff;--sd-color-info-text: #fff;--sd-color-warning-text: #212529;--sd-color-danger-text: #fff;--sd-color-light-text: #212529;--sd-color-muted-text: #fff;--sd-color-dark-text: #fff;--sd-color-black-text: #fff;--sd-color-white-text: #212529;--sd-color-shadow: rgba(0, 0, 0, 0.15);--sd-color-card-border: rgba(0, 0, 0, 0.125);--sd-color-card-border-hover: hsla(231, 99%, 66%, 1);--sd-color-card-background: transparent;--sd-color-card-text: inherit;--sd-color-card-header: transparent;--sd-color-card-footer: transparent;--sd-color-tabs-label-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-hover: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-inactive: hsl(0, 0%, 66%);--sd-color-tabs-underline-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-underline-hover: rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem;--sd-fontsize-dropdown: inherit;--sd-fontsize-dropdown-title: 1rem;--sd-fontweight-dropdown-title: 700}
diff --git a/2025.0/_static/_sphinx_javascript_frameworks_compat.js b/2025.0/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000000..81415803ec
--- /dev/null
+++ b/2025.0/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/2025.0/_static/basic.css b/2025.0/_static/basic.css
new file mode 100644
index 0000000000..7ebbd6d07b
--- /dev/null
+++ b/2025.0/_static/basic.css
@@ -0,0 +1,914 @@
+/*
+ * Sphinx stylesheet -- basic theme.
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin-top: 10px;
+}
+
+ul.search li {
+    padding: 5px 0;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a:visited {
+    color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/2025.0/_static/check-solid.svg b/2025.0/_static/check-solid.svg
new file mode 100644
index 0000000000..92fad4b5c0
--- /dev/null
+++ b/2025.0/_static/check-solid.svg
@@ -0,0 +1,4 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>
diff --git a/2025.0/_static/clipboard.min.js b/2025.0/_static/clipboard.min.js
new file mode 100644
index 0000000000..54b3c46381
--- /dev/null
+++ b/2025.0/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},i="";return"string"==typeof t?(e=t,n="rtl"===document.documentElement.getAttribute("dir"),(o=document.createElement("textarea")).style.fontSize="12pt",o.style.border="0",o.style.padding="0",o.style.margin="0",o.style.position="absolute",o.style[n?"right":"left"]="-9999px",n=window.pageYOffset||document.documentElement.scrollTop,o.style.top="".concat(n,"px"),o.setAttribute("readonly",""),o.value=e,o=o,r.container.appendChild(o),i=c()(o),a("copy"),o.remove()):(i=c()(t),a("copy")),i};function r(t){return(r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==r(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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t=o?(t=m(this).constructor,Reflect.construct(e,arguments,t)):e.apply(this,arguments),e=this,!(t=t)||"object"!==d(t)&&"function"!=typeof t?function(t){if(void 0!==t)return t;throw new ReferenceError("this hasn't been initialised - super() hasn't been called")}(e):t}}function m(t){return(m=Object.setPrototypeOf?Object.getPrototypeOf:function(t){return t.__proto__||Object.getPrototypeOf(t)})(t)}function v(t,e){t="data-clipboard-".concat(t);if(e.hasAttribute(t))return e.getAttribute(t)}var o=function(){!function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),e&&y(t,e)}(r,i());var t,e,n,o=h(r);function r(t,e){var n;return function(t){if(!(t instanceof r))throw new TypeError("Cannot call a class as a function")}(this),(n=o.call(this)).resolveOptions(e),n.listenClick(t),n}return t=r,n=[{key:"copy",value:function(t){var 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e=t.delegateTarget||t.currentTarget,t=s({action:this.action(e),container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:this.action,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&p(t.prototype,e),n&&p(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function 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e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/2025.0/_static/copy-button.svg b/2025.0/_static/copy-button.svg
new file mode 100644
index 0000000000..9c074dae52
--- /dev/null
+++ b/2025.0/_static/copy-button.svg
@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>
diff --git a/2025.0/_static/copybutton.css b/2025.0/_static/copybutton.css
new file mode 100644
index 0000000000..f1916ec7d1
--- /dev/null
+++ b/2025.0/_static/copybutton.css
@@ -0,0 +1,94 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .3em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+    color: #22863a;
+}
+
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/2025.0/_static/copybutton.js b/2025.0/_static/copybutton.js
new file mode 100644
index 0000000000..2ea7ff3e21
--- /dev/null
+++ b/2025.0/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_success']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_to_clipboard']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>`
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      ${iconCopy}
+    </button>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/2025.0/_static/copybutton_funcs.js b/2025.0/_static/copybutton_funcs.js
new file mode 100644
index 0000000000..dbe1aaad79
--- /dev/null
+++ b/2025.0/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/2025.0/_static/css/badge_only.css b/2025.0/_static/css/badge_only.css
new file mode 100644
index 0000000000..88ba55b965
--- /dev/null
+++ b/2025.0/_static/css/badge_only.css
@@ -0,0 +1 @@
+.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions .rst-other-versions .rtd-current-item{font-weight:700}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}#flyout-search-form{padding:6px}
\ No newline at end of file
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diff --git a/2025.0/_static/css/fonts/Roboto-Slab-Regular.woff b/2025.0/_static/css/fonts/Roboto-Slab-Regular.woff
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diff --git a/2025.0/_static/css/fonts/fontawesome-webfont.eot b/2025.0/_static/css/fonts/fontawesome-webfont.eot
new file mode 100644
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diff --git a/2025.0/_static/css/fonts/fontawesome-webfont.svg b/2025.0/_static/css/fonts/fontawesome-webfont.svg
new file mode 100644
index 0000000000..855c845e53
--- /dev/null
+++ b/2025.0/_static/css/fonts/fontawesome-webfont.svg
@@ -0,0 +1,2671 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg>
+<metadata>
+Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016
+ By ,,,
+Copyright Dave Gandy 2016. All rights reserved.
+</metadata>
+<defs>
+<font id="FontAwesome" horiz-adv-x="1536" >
+  <font-face 
+    font-family="FontAwesome"
+    font-weight="400"
+    font-stretch="normal"
+    units-per-em="1792"
+    panose-1="0 0 0 0 0 0 0 0 0 0"
+    ascent="1536"
+    descent="-256"
+    bbox="-1.02083 -256.962 2304.6 1537.02"
+    underline-thickness="0"
+    underline-position="0"
+    unicode-range="U+0020-F500"
+  />
+<missing-glyph horiz-adv-x="896" 
+d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
+    <glyph glyph-name=".notdef" horiz-adv-x="896" 
+d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
+    <glyph glyph-name=".null" horiz-adv-x="0" 
+ />
+    <glyph glyph-name="nonmarkingreturn" horiz-adv-x="597" 
+ />
+    <glyph glyph-name="space" unicode=" " horiz-adv-x="448" 
+ />
+    <glyph glyph-name="dieresis" unicode="&#xa8;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="copyright" unicode="&#xa9;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="registered" unicode="&#xae;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="acute" unicode="&#xb4;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="AE" unicode="&#xc6;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="Oslash" unicode="&#xd8;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="trademark" unicode="&#x2122;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="infinity" unicode="&#x221e;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="notequal" unicode="&#x2260;" horiz-adv-x="1792" 
+ />
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dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt:first-child{margin-top:0}html.writer-html4 .rst-content dl:not(.docutils) code.descclassname,html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descclassname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{background-color:transparent;border:none;padding:0;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .optional,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .optional{display:inline-block;padding:0 4px;color:#000;font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .property,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .property{display:inline-block;padding-right:8px;max-width:100%}html.writer-html4 .rst-content dl:not(.docutils) .k,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .k{font-style:italic}html.writer-html4 .rst-content dl:not(.docutils) .descclassname,html.writer-html4 .rst-content dl:not(.docutils) .descname,html.writer-html4 .rst-content dl:not(.docutils) .sig-name,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .sig-name{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;color:#000}.rst-content .viewcode-back,.rst-content .viewcode-link{display:inline-block;color:#27ae60;font-size:80%;padding-left:24px}.rst-content .viewcode-back{display:block;float:right}.rst-content p.rubric{margin-bottom:12px;font-weight:700}.rst-content code.download,.rst-content tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel,.rst-content .menuselection{font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content .guilabel,.rst-content .menuselection{border:1px solid #7fbbe3;background:#e7f2fa}.rst-content 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format("woff2"),url(fonts/Roboto-Slab-Bold.woff?bed5564a116b05148e3b3bea6fb1162a) format("woff");font-display:block}
\ No newline at end of file
diff --git a/2023.2/_static/style.css b/2025.0/_static/custom.css
similarity index 89%
rename from 2023.2/_static/style.css
rename to 2025.0/_static/custom.css
index b631dcb91b..1db55bd294 100644
--- a/2023.2/_static/style.css
+++ b/2025.0/_static/custom.css
@@ -120,3 +120,12 @@ div.quotation {
 }
 
 a#wap_dns {display: none;}
+
+
+button#version-switcher-button {
+   background-color:  #2980b9;
+}
+
+div#version-switcher-dropdown {
+   display: inline;
+}
diff --git a/2025.0/_static/design-tabs.js b/2025.0/_static/design-tabs.js
new file mode 100644
index 0000000000..b25bd6a4fa
--- /dev/null
+++ b/2025.0/_static/design-tabs.js
@@ -0,0 +1,101 @@
+// @ts-check
+
+// Extra JS capability for selected tabs to be synced
+// The selection is stored in local storage so that it persists across page loads.
+
+/**
+ * @type {Record<string, HTMLElement[]>}
+ */
+let sd_id_to_elements = {};
+const storageKeyPrefix = "sphinx-design-tab-id-";
+
+/**
+ * Create a key for a tab element.
+ * @param {HTMLElement} el - The tab element.
+ * @returns {[string, string, string] | null} - The key.
+ *
+ */
+function create_key(el) {
+  let syncId = el.getAttribute("data-sync-id");
+  let syncGroup = el.getAttribute("data-sync-group");
+  if (!syncId || !syncGroup) return null;
+  return [syncGroup, syncId, syncGroup + "--" + syncId];
+}
+
+/**
+ * Initialize the tab selection.
+ *
+ */
+function ready() {
+  // Find all tabs with sync data
+
+  /** @type {string[]} */
+  let groups = [];
+
+  document.querySelectorAll(".sd-tab-label").forEach((label) => {
+    if (label instanceof HTMLElement) {
+      let data = create_key(label);
+      if (data) {
+        let [group, id, key] = data;
+
+        // add click event listener
+        // @ts-ignore
+        label.onclick = onSDLabelClick;
+
+        // store map of key to elements
+        if (!sd_id_to_elements[key]) {
+          sd_id_to_elements[key] = [];
+        }
+        sd_id_to_elements[key].push(label);
+
+        if (groups.indexOf(group) === -1) {
+          groups.push(group);
+          // Check if a specific tab has been selected via URL parameter
+          const tabParam = new URLSearchParams(window.location.search).get(
+            group
+          );
+          if (tabParam) {
+            console.log(
+              "sphinx-design: Selecting tab id for group '" +
+                group +
+                "' from URL parameter: " +
+                tabParam
+            );
+            window.sessionStorage.setItem(storageKeyPrefix + group, tabParam);
+          }
+        }
+
+        // Check is a specific tab has been selected previously
+        let previousId = window.sessionStorage.getItem(
+          storageKeyPrefix + group
+        );
+        if (previousId === id) {
+          // console.log(
+          //   "sphinx-design: Selecting tab from session storage: " + id
+          // );
+          // @ts-ignore
+          label.previousElementSibling.checked = true;
+        }
+      }
+    }
+  });
+}
+
+/**
+ *  Activate other tabs with the same sync id.
+ *
+ * @this {HTMLElement} - The element that was clicked.
+ */
+function onSDLabelClick() {
+  let data = create_key(this);
+  if (!data) return;
+  let [group, id, key] = data;
+  for (const label of sd_id_to_elements[key]) {
+    if (label === this) continue;
+    // @ts-ignore
+    label.previousElementSibling.checked = true;
+  }
+  window.sessionStorage.setItem(storageKeyPrefix + group, id);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/2025.0/_static/doctools.js b/2025.0/_static/doctools.js
new file mode 100644
index 0000000000..0398ebb9f0
--- /dev/null
+++ b/2025.0/_static/doctools.js
@@ -0,0 +1,149 @@
+/*
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/2025.0/_static/documentation_options.js b/2025.0/_static/documentation_options.js
new file mode 100644
index 0000000000..43702bc306
--- /dev/null
+++ b/2025.0/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '2025.0',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/2025.0/_static/favicons.png b/2025.0/_static/favicons.png
new file mode 100644
index 0000000000..f450376b19
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diff --git a/2025.0/_static/file.png b/2025.0/_static/file.png
new file mode 100644
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diff --git a/2025.0/_static/fonts/Lato/lato-bold.eot b/2025.0/_static/fonts/Lato/lato-bold.eot
new file mode 100644
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diff --git a/2025.0/_static/fonts/Lato/lato-bold.ttf b/2025.0/_static/fonts/Lato/lato-bold.ttf
new file mode 100644
index 0000000000..29f691d5ed
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diff --git a/2025.0/_static/fonts/Lato/lato-bold.woff b/2025.0/_static/fonts/Lato/lato-bold.woff
new file mode 100644
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diff --git a/2025.0/_static/fonts/Lato/lato-bold.woff2 b/2025.0/_static/fonts/Lato/lato-bold.woff2
new file mode 100644
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Binary files /dev/null and b/2025.0/_static/fonts/Lato/lato-bold.woff2 differ
diff --git a/2025.0/_static/fonts/Lato/lato-bolditalic.eot b/2025.0/_static/fonts/Lato/lato-bolditalic.eot
new file mode 100644
index 0000000000..3d4154936b
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diff --git a/2025.0/_static/fonts/Lato/lato-bolditalic.ttf b/2025.0/_static/fonts/Lato/lato-bolditalic.ttf
new file mode 100644
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Binary files /dev/null and b/2025.0/_static/fonts/Lato/lato-bolditalic.ttf differ
diff --git a/2025.0/_static/fonts/Lato/lato-bolditalic.woff b/2025.0/_static/fonts/Lato/lato-bolditalic.woff
new file mode 100644
index 0000000000..88ad05b9ff
Binary files /dev/null and b/2025.0/_static/fonts/Lato/lato-bolditalic.woff differ
diff --git a/2025.0/_static/fonts/Lato/lato-bolditalic.woff2 b/2025.0/_static/fonts/Lato/lato-bolditalic.woff2
new file mode 100644
index 0000000000..c4e3d804b5
Binary files /dev/null and b/2025.0/_static/fonts/Lato/lato-bolditalic.woff2 differ
diff --git a/2025.0/_static/fonts/Lato/lato-italic.eot b/2025.0/_static/fonts/Lato/lato-italic.eot
new file mode 100644
index 0000000000..3f826421a1
Binary files /dev/null and b/2025.0/_static/fonts/Lato/lato-italic.eot differ
diff --git a/2025.0/_static/fonts/Lato/lato-italic.ttf b/2025.0/_static/fonts/Lato/lato-italic.ttf
new file mode 100644
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diff --git a/2025.0/_static/fonts/Lato/lato-italic.woff b/2025.0/_static/fonts/Lato/lato-italic.woff
new file mode 100644
index 0000000000..76114bc033
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diff --git a/2025.0/_static/fonts/Lato/lato-italic.woff2 b/2025.0/_static/fonts/Lato/lato-italic.woff2
new file mode 100644
index 0000000000..3404f37e2e
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diff --git a/2025.0/_static/fonts/Lato/lato-regular.eot b/2025.0/_static/fonts/Lato/lato-regular.eot
new file mode 100644
index 0000000000..11e3f2a5f0
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diff --git a/2025.0/_static/fonts/Lato/lato-regular.ttf b/2025.0/_static/fonts/Lato/lato-regular.ttf
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diff --git a/2025.0/_static/fonts/Lato/lato-regular.woff b/2025.0/_static/fonts/Lato/lato-regular.woff
new file mode 100644
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new file mode 100644
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diff --git a/2025.0/_static/fonts/RobotoSlab/roboto-slab-v7-bold.eot b/2025.0/_static/fonts/RobotoSlab/roboto-slab-v7-bold.eot
new file mode 100644
index 0000000000..79dc8efed3
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\ No newline at end of file
diff --git a/2025.0/_static/js/versions.js b/2025.0/_static/js/versions.js
new file mode 100644
index 0000000000..4958195e07
--- /dev/null
+++ b/2025.0/_static/js/versions.js
@@ -0,0 +1,228 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = true;
+const themeLanguageSelector = true;
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    // Insert the current language to the options on the selector
+    let languages = config.projects.translations.concat(config.projects.current);
+    languages = languages.sort((a, b) => a.language.name.localeCompare(b.language.name));
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${languages
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/2025.0/_static/language_data.js b/2025.0/_static/language_data.js
new file mode 100644
index 0000000000..c7fe6c6faf
--- /dev/null
+++ b/2025.0/_static/language_data.js
@@ -0,0 +1,192 @@
+/*
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/2025.0/_static/minus.png b/2025.0/_static/minus.png
new file mode 100644
index 0000000000..d96755fdaf
Binary files /dev/null and b/2025.0/_static/minus.png differ
diff --git a/2025.0/_static/nbsphinx-broken-thumbnail.svg b/2025.0/_static/nbsphinx-broken-thumbnail.svg
new file mode 100644
index 0000000000..4919ca8829
--- /dev/null
+++ b/2025.0/_static/nbsphinx-broken-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: lightcoral; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: crimson; }
+    }
+  </style>
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new file mode 100644
index 0000000000..a3fb27c30f
--- /dev/null
+++ b/2025.0/_static/nbsphinx-code-cells.css
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+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+    background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
+    }
+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
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+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
+}
+
+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+    color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+    color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+    width: 4.5ex;
+    padding-top: 5px;
+    position: relative;
+    user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+    position: absolute;
+    right: 0;
+    margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+    div.nbinput.container div.prompt,
+    div.nboutput.container div.prompt {
+        width: unset;
+        text-align: left;
+        padding: 0.4em;
+    }
+    div.nboutput.container div.prompt.empty {
+        padding: 0;
+    }
+
+    div.nbinput.container div.prompt > div,
+    div.nboutput.container div.prompt > div {
+        position: unset;
+    }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+    overflow: hidden;
+    white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+    -webkit-flex: 1;
+    flex: 1;
+    overflow: auto;
+}
+@media (max-width: 540px) {
+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
+        width: 100%;
+    }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+    border: 1px solid #e0e0e0;
+    border-radius: 2px;
+    /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
+    text-align: left !important;
+}
+
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+div.nboutput.container div.math p {
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+}
+
+/* standard error */
+div.nboutput.container div.output_area.stderr {
+    background: #fdd;
+}
+
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+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
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+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+    display: none;
+}
+
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+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+  font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
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+}
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new file mode 100644
index 0000000000..365c27a96b
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+.nbsphinx-gallery {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 5px;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+    padding: 5px;
+    border: 1px dotted currentColor;
+    border-radius: 2px;
+    text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+    border-style: solid;
+}
+
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+    max-width: 100%;
+    max-height: 100%;
+}
+
+.nbsphinx-gallery > a > div:first-child {
+    display: flex;
+    align-items: start;
+    justify-content: center;
+    height: 120px;
+    margin-bottom: 5px;
+}
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new file mode 100644
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+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
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+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
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\ No newline at end of file
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diff --git a/2025.0/_static/searchtools.js b/2025.0/_static/searchtools.js
new file mode 100644
index 0000000000..2c774d17af
--- /dev/null
+++ b/2025.0/_static/searchtools.js
@@ -0,0 +1,632 @@
+/*
+ * Sphinx JavaScript utilities for the full-text search.
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename, kind] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+// Global search result kind enum, used by themes to style search results.
+class SearchResultKind {
+    static get index() { return  "index"; }
+    static get object() { return "object"; }
+    static get text() { return "text"; }
+    static get title() { return "title"; }
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename, kind] = item;
+
+  let listItem = document.createElement("li");
+  // Add a class representing the item's type:
+  // can be used by a theme's CSS selector for styling
+  // See SearchResultKind for the class names.
+  listItem.classList.add(`kind-${kind}`);
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = Documentation.ngettext(
+      "Search finished, found one page matching the search query.",
+      "Search finished, found ${resultCount} pages matching the search query.",
+      resultCount,
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlink", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.setAttribute("role", "list");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename, kind].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score + boost,
+            filenames[file],
+            SearchResultKind.title,
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+            SearchResultKind.index,
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+        SearchResultKind.object,
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+        SearchResultKind.text,
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/2025.0/_static/sphinx-design.min.css b/2025.0/_static/sphinx-design.min.css
new file mode 100644
index 0000000000..860c36da0f
--- /dev/null
+++ b/2025.0/_static/sphinx-design.min.css
@@ -0,0 +1 @@
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diff --git a/2025.0/_static/sphinx_highlight.js b/2025.0/_static/sphinx_highlight.js
new file mode 100644
index 0000000000..8a96c69a19
--- /dev/null
+++ b/2025.0/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/2025.0/_static/tabs.css b/2025.0/_static/tabs.css
new file mode 100644
index 0000000000..957ba60d69
--- /dev/null
+++ b/2025.0/_static/tabs.css
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+.sphinx-tabs {
+  margin-bottom: 1rem;
+}
+
+[role="tablist"] {
+  border-bottom: 1px solid #a0b3bf;
+}
+
+.sphinx-tabs-tab {
+  position: relative;
+  font-family: Lato,'Helvetica Neue',Arial,Helvetica,sans-serif;
+  color: #1D5C87;
+  line-height: 24px;
+  margin: 0;
+  font-size: 16px;
+  font-weight: 400;
+  background-color: rgba(255, 255, 255, 0);
+  border-radius: 5px 5px 0 0;
+  border: 0;
+  padding: 1rem 1.5rem;
+  margin-bottom: 0;
+}
+
+.sphinx-tabs-tab[aria-selected="true"] {
+  font-weight: 700;
+  border: 1px solid #a0b3bf;
+  border-bottom: 1px solid white;
+  margin: -1px;
+  background-color: white;
+}
+
+.sphinx-tabs-tab:focus {
+  z-index: 1;
+  outline-offset: 1px;
+}
+
+.sphinx-tabs-panel {
+  position: relative;
+  padding: 1rem;
+  border: 1px solid #a0b3bf;
+  margin: 0px -1px -1px -1px;
+  border-radius: 0 0 5px 5px;
+  border-top: 0;
+  background: white;
+}
+
+.sphinx-tabs-panel.code-tab {
+  padding: 0.4rem;
+}
+
+.sphinx-tab img {
+	margin-bottom: 24 px;
+}
+
+/* Dark theme preference styling */
+
+@media (prefers-color-scheme: dark) {
+  body[data-theme="auto"] .sphinx-tabs-panel {
+    color: white;
+    background-color: rgb(50, 50, 50);
+  }
+
+  body[data-theme="auto"] .sphinx-tabs-tab {
+    color: white;
+    background-color: rgba(255, 255, 255, 0.05);
+  }
+
+  body[data-theme="auto"] .sphinx-tabs-tab[aria-selected="true"] {
+    border-bottom: 1px solid rgb(50, 50, 50);
+    background-color: rgb(50, 50, 50);
+  }
+}
+
+/* Explicit dark theme styling */
+
+body[data-theme="dark"] .sphinx-tabs-panel {
+  color: white;
+  background-color: rgb(50, 50, 50);
+}
+
+body[data-theme="dark"] .sphinx-tabs-tab {
+  color: white;
+  background-color: rgba(255, 255, 255, 0.05);
+}
+
+body[data-theme="dark"] .sphinx-tabs-tab[aria-selected="true"] {
+  border-bottom: 2px solid rgb(50, 50, 50);
+  background-color: rgb(50, 50, 50);
+}
diff --git a/2025.0/_static/tabs.js b/2025.0/_static/tabs.js
new file mode 100644
index 0000000000..48dc303c8c
--- /dev/null
+++ b/2025.0/_static/tabs.js
@@ -0,0 +1,145 @@
+try {
+  var session = window.sessionStorage || {};
+} catch (e) {
+  var session = {};
+}
+
+window.addEventListener("DOMContentLoaded", () => {
+  const allTabs = document.querySelectorAll('.sphinx-tabs-tab');
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+  allTabs.forEach(tab => {
+    tab.addEventListener("click", changeTabs);
+  });
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+  tabLists.forEach(tabList => {
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+  });
+
+  // Restore group tab selection from session
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+  if (lastSelected != null) selectNamedTabs(lastSelected);
+});
+
+/**
+ * Key focus left and right between sibling elements using arrows
+ * @param  {Node} e the element in focus when key was pressed
+ */
+function keyTabs(e) {
+    const tab = e.target;
+    let nextTab = null;
+    if (e.keyCode === 39 || e.keyCode === 37) {
+      tab.setAttribute("tabindex", -1);
+      // Move right
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+      // Move left
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+        if (nextTab === null) {
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+      }
+    }
+
+    if (nextTab !== null) {
+      nextTab.setAttribute("tabindex", 0);
+      nextTab.focus();
+    }
+}
+
+/**
+ * Select or deselect clicked tab. If a group tab
+ * is selected, also select tab in other tabLists.
+ * @param  {Node} e the element that was clicked
+ */
+function changeTabs(e) {
+  // Use this instead of the element that was clicked, in case it's a child
+  const notSelected = this.getAttribute("aria-selected") === "false";
+  const positionBefore = this.parentNode.getBoundingClientRect().top;
+  const notClosable = !this.parentNode.classList.contains("closeable");
+
+  deselectTabList(this);
+
+  if (notSelected || notClosable) {
+    selectTab(this);
+    const name = this.getAttribute("name");
+    selectNamedTabs(name, this.id);
+
+    if (this.classList.contains("group-tab")) {
+      // Persist during session
+      session.setItem('sphinx-tabs-last-selected', name);
+    }
+  }
+
+  const positionAfter = this.parentNode.getBoundingClientRect().top;
+  const positionDelta = positionAfter - positionBefore;
+  // Scroll to offset content resizing
+  window.scrollTo(0, window.scrollY + positionDelta);
+}
+
+/**
+ * Select tab and show associated panel.
+ * @param  {Node} tab tab to select
+ */
+function selectTab(tab) {
+  tab.setAttribute("aria-selected", true);
+
+  // Show the associated panel
+  document
+    .getElementById(tab.getAttribute("aria-controls"))
+    .removeAttribute("hidden");
+}
+
+/**
+ * Hide the panels associated with all tabs within the
+ * tablist containing this tab.
+ * @param  {Node} tab a tab within the tablist to deselect
+ */
+function deselectTabList(tab) {
+  const parent = tab.parentNode;
+  const grandparent = parent.parentNode;
+
+  Array.from(parent.children)
+  .forEach(t => t.setAttribute("aria-selected", false));
+
+  Array.from(grandparent.children)
+    .slice(1)  // Skip tablist
+    .forEach(panel => panel.setAttribute("hidden", true));
+}
+
+/**
+ * Select grouped tabs with the same name, but no the tab
+ * with the given id.
+ * @param  {Node} name name of grouped tab to be selected
+ * @param  {Node} clickedId id of clicked tab
+ */
+function selectNamedTabs(name, clickedId=null) {
+  const groupedTabs = document.querySelectorAll(`.sphinx-tabs-tab[name="${name}"]`);
+  const tabLists = Array.from(groupedTabs).map(tab => tab.parentNode);
+
+  tabLists
+    .forEach(tabList => {
+      // Don't want to change the tabList containing the clicked tab
+      const clickedTab = tabList.querySelector(`[id="${clickedId}"]`);
+      if (clickedTab === null ) {
+        // Select first tab with matching name
+        const tab = tabList.querySelector(`.sphinx-tabs-tab[name="${name}"]`);
+        deselectTabList(tab);
+        selectTab(tab);
+      }
+    })
+}
+
+if (typeof exports === 'undefined') {
+  exports = {};
+}
+
+exports.keyTabs = keyTabs;
+exports.changeTabs = changeTabs;
+exports.selectTab = selectTab;
+exports.deselectTabList = deselectTabList;
+exports.selectNamedTabs = selectNamedTabs;
diff --git a/2025.0/_static/version_switcher.js b/2025.0/_static/version_switcher.js
new file mode 100644
index 0000000000..162851d217
--- /dev/null
+++ b/2025.0/_static/version_switcher.js
@@ -0,0 +1,31 @@
+function load_versions(json){
+    var button = document.getElementById('version-switcher-button')
+    var container = document.getElementById('version-switcher-dropdown')
+    var loc = window.location.href;
+    var s = document.createElement('select');
+    s.style = "border-radius:5px;"
+    const versions = JSON.parse(json);
+    for (entry of versions){
+        var o = document.createElement('option');
+        var optionText = '';
+        if ('name' in entry){
+            optionText = entry.name;
+        }else{
+            optionText = entry.version;
+        }
+        o.value = entry.url;
+        if (current_version == entry.version){
+            o.selected = true;
+        }
+        o.innerHTML = optionText;
+        s.append(o);
+    }
+    s.addEventListener("change", ()=> {
+        var current_url = new URL(window.location.href);
+        var path = current_url.pathname;
+        //strip version from path
+        var page_path = path.substring(project_name.length+current_version.length+3);
+        window.location.href = s.value + page_path;
+    });
+    container.append(s);
+}
diff --git a/2025.0/algorithms.html b/2025.0/algorithms.html
new file mode 100644
index 0000000000..a9c20fa056
--- /dev/null
+++ b/2025.0/algorithms.html
@@ -0,0 +1,1238 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Supported Algorithms &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+      <link rel="stylesheet" type="text/css" href="_static/custom.css?v=132153fd" />
+      <link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
+
+  
+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="_static/documentation_options.js?v=0e25812e"></script>
+      <script src="_static/doctools.js?v=9bcbadda"></script>
+      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="_static/copybutton.js?v=f281be69"></script>
+      <script src="_static/version_switcher.js?v=107152b5"></script>
+      <script src="_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" href="oneapi-gpu.html" />
+    <link rel="prev" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" href="kaggle/automl.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
+          (function () {
+            var url = 'https://www.intel.com/content/dam/www/global/wap/tms-loader.js'; // WAP file URL
+            var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = url;
+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
+          })();
+      }
+      </script>
+
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
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+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li class="breadcrumb-item active">Supported Algorithms</li>
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+            <a href="_sources/algorithms.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="supported-algorithms">
+<span id="sklearn-algorithms"></span><h1>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Link to this heading"></a></h1>
+<p>Applying Intel® Extension for Scikit-learn* impacts the following scikit-learn algorithms:</p>
+<section id="on-cpu">
+<h2>on CPU<a class="headerlink" href="#on-cpu" title="Link to this heading"></a></h2>
+<section id="classification">
+<h3>Classification<a class="headerlink" href="#classification" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>SVC</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>NuSVC</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>RandomForestClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsClassifier</cite></p></td>
+<td><ul>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == <cite>‘kd_tree’</cite>:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p>
+</li>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == <cite>‘brute’</cite>:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p>
+</li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LogisticRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> not in [<cite>‘lbfgs’</cite>, <cite>‘newton-cg’</cite>]</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="regression">
+<h3>Regression<a class="headerlink" href="#regression" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>SVR</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>NuSVR</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>RandomForestRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LinearRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported.</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>Ridge</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> != <cite>‘auto’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>ElasticNet</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>Lasso</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="clustering">
+<h3>Clustering<a class="headerlink" href="#clustering" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>KMeans</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>DBSCAN</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> not in [<cite>‘brute’</cite>, <cite>‘auto’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="dimensionality-reduction">
+<h3>Dimensionality Reduction<a class="headerlink" href="#dimensionality-reduction" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>PCA</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>IncrementalPCA</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>TSNE</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != ‘euclidean’ or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+<p>Refer to <a class="reference internal" href="guide/acceleration.html#acceleration-tsne"><span class="std std-ref">TSNE acceleration details</span></a> to learn more.</p>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="nearest-neighbors">
+<h3>Nearest Neighbors<a class="headerlink" href="#nearest-neighbors" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>NearestNeighbors</cite></p></td>
+<td><ul>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == ‘kd_tree’:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p>
+</li>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == ‘brute’:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p>
+</li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="other-tasks">
+<h3>Other Tasks<a class="headerlink" href="#other-tasks" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>EmpiricalCovariance</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>train_test_split</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>assert_all_finite</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>pairwise_distance</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘cosine’</cite>, <cite>‘correlation’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>roc_auc_score</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">average</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">max_fpr</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">multi_class</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>No limitations</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="on-gpu">
+<h2>on GPU<a class="headerlink" href="#on-gpu" title="Link to this heading"></a></h2>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="oneapi-gpu.html#oneapi-gpu"><span class="std std-ref">oneAPI and GPU support in Intel® Extension for Scikit-learn*</span></a></p>
+</div>
+<section id="id1">
+<h3>Classification<a class="headerlink" href="#id1" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>SVC</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">kernel</span></code> = <cite>‘sigmoid_poly’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only binary dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>RandomForestClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>KNeighborsClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>LogisticRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> != <cite>‘newton-cg’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">penalty</span></code> != <cite>‘l2’</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id2">
+<h3>Regression<a class="headerlink" href="#id2" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>RandomForestRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LinearRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id3">
+<h3>Clustering<a class="headerlink" href="#id3" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>KMeans</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">Init</span></code> = <cite>‘k-means++’</cite> fallbacks to CPU.</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>DBSCAN</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> not in [<cite>‘brute’</cite>, <cite>‘auto’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id4">
+<h3>Dimensionality Reduction<a class="headerlink" href="#id4" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>PCA</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id5">
+<h3>Nearest Neighbors<a class="headerlink" href="#id5" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>NearestNeighbors</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id6">
+<h3>Other Tasks<a class="headerlink" href="#id6" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>EmpiricalCovariance</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="spmd-support">
+<h2>SPMD Support<a class="headerlink" href="#spmd-support" title="Link to this heading"></a></h2>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="distributed-mode.html#distributed"><span class="std std-ref">Distributed Mode</span></a></p>
+</div>
+<section id="id7">
+<h3>Classification<a class="headerlink" href="#id7" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>RandomForestClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> method not supported</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LogisticRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> != <cite>‘newton-cg’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">penalty</span></code> != <cite>‘l2’</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id8">
+<h3>Regression<a class="headerlink" href="#id8" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>RandomForestRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LinearRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id9">
+<h3>Clustering<a class="headerlink" href="#id9" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>KMeans</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">Init</span></code> = <cite>‘k-means++’</cite> fallbacks to CPU.</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>DBSCAN</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> not in [<cite>‘brute’</cite>, <cite>‘auto’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id10">
+<h3>Dimensionality Reduction<a class="headerlink" href="#id10" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>PCA</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fit</span></code> is the only method supported</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id11">
+<h3>Nearest Neighbors<a class="headerlink" href="#id11" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>NearestNeighbors</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id12">
+<h3>Other Tasks<a class="headerlink" href="#id12" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>EmpiricalCovariance</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="scikit-learn-tests">
+<h2>Scikit-learn Tests<a class="headerlink" href="#scikit-learn-tests" title="Link to this heading"></a></h2>
+<p>Monkey-patched scikit-learn classes and functions passes scikit-learn’s own test
+suite, with few exceptions, specified in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/deselected_tests.yaml">deselected_tests.yaml</a>.</p>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="kaggle/automl.html" class="btn btn-neutral float-left" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="oneapi-gpu.html" class="btn btn-neutral float-right" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+            <a href="_sources/array_api.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="array-api-support">
+<span id="array-api"></span><h1>Array API support<a class="headerlink" href="#array-api-support" title="Link to this heading"></a></h1>
+<p>The <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API</a> specification defines
+a standard API for all array manipulation libraries with a NumPy-like API.
+Intel(R) Extension for Scikit-Learn doesn’t require
+<a class="reference external" href="https://github.com/data-apis/array-api-compat">array-api-compat</a> to be installed for
+functional support of the array API standard.
+In the current implementation, the functional support of array api follows the functional
+support of different array or DataFrame inputs and does not modify the precision of the
+input and output data formats unless necessary. Any array API input will be converted to host
+numpy.ndarrays and all internal manipulations with data will be done with these representations of
+the input data. DPNP’s ‘ndarray’ and Data Parallel Control’s ‘usm_ndarray’ have special handling
+requirements that are described in the relevant section of this document. Output values will in
+all relevant cases match the input data format.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Currently, only <a class="reference external" href="https://github.com/data-apis/array-api-strict">array-api-strict</a>,
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a>, <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp</a>
+and <a class="reference external" href="https://numpy.org/">numpy</a> are known to work with sklearnex estimators.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Stock Scikit-learn’s array API support requires <a class="reference external" href="https://github.com/data-apis/array-api-compat">array-api-compat</a> to be installed.</p>
+</div>
+<section id="support-for-dpnp-and-dpctl">
+<h2>Support for DPNP and DPCTL<a class="headerlink" href="#support-for-dpnp-and-dpctl" title="Link to this heading"></a></h2>
+<p>The functional support of input data for sklearnex estimators also extended for SYCL USM array types.
+These include SYCL USM arrays <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp’s</a> ndarray and
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">Data Parallel Control usm_ndarray</a>.
+DPNP ndarray and Data Parallel Control usm_ndarray contain SYCL contexts which can be used for
+<cite>sklearnex</cite> device offloading.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Current support for DPNP and DPCTL usm_ndarray data can be copied and moved to and from device in sklearnex and have
+impacts on memory utilization.</p>
+</div>
+<p>DPCTL or DPNP inputs are not required to use <cite>config_context(target_offload=device)</cite>.
+<cite>sklearnex</cite> will use input usm_ndarray sycl context for device offloading.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>As DPCTL or DPNP inputs contain SYCL contexts, they do not require <cite>config_context(target_offload=device)</cite>.
+However, the use of <cite>config_context`</cite> will override the contained SYCL context and will force movement
+of data to the targeted device.</p>
+</div>
+</section>
+<section id="support-for-array-api-compatible-inputs">
+<h2>Support for Array API-compatible inputs<a class="headerlink" href="#support-for-array-api-compatible-inputs" title="Link to this heading"></a></h2>
+<p>All patched estimators, metrics, tools and non-scikit-learn estimators functionally support Array API standard.
+Intel(R) Extension for scikit-Learn preserves input data format for all outputs. For all array inputs except
+SYCL USM arrays <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp’s</a> ndarray and
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">Data Parallel Control usm_ndarray</a> all computation
+will be only accomplished on CPU unless specified by a <cite>config_context`</cite> with an available GPU device.</p>
+<p>Stock scikit-learn uses <cite>config_context(array_api_dispatch=True)</cite> for enabling Array API
+<a class="reference external" href="https://scikit-learn.org/1.5/modules/array_api.html">support</a>.
+If <cite>array_api_dispatch</cite> is enabled and the installed Scikit-Learn version supports array API, then the original
+inputs are used when falling back to Scikit-Learn functionality.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Data Parallel Control usm_ndarray or DPNP ndarray inputs will use host numpy data copies when
+falling back to Scikit-Learn since they are not array API compliant.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Functional support doesn’t guarantee that after the model is trained, fitted attributes that are arrays
+will also be from the same namespace as the training data.</p>
+</div>
+</section>
+<section id="example-usage">
+<h2>Example usage<a class="headerlink" href="#example-usage" title="Link to this heading"></a></h2>
+<section id="dpnp-ndarrays">
+<h3>DPNP ndarrays<a class="headerlink" href="#dpnp-ndarrays" title="Link to this heading"></a></h3>
+<p>Here is an example code to demonstrate how to use <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp</a> arrays to
+run <cite>RandomForestRegressor</cite> on a GPU without <cite>config_context(array_api_dispatch=True)</cite>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2023 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="c1"># sklearnex RF example for GPU offloading with DPNP ndarray:</span>
+<span class="c1">#    python ./random_forest_regressor_dpnp_batch.py</span>
+
+<span class="kn">import</span> <span class="nn">dpnp</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+
+<span class="kn">from</span> <span class="nn">sklearnex.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
+
+<span class="n">sycl_device</span> <span class="o">=</span> <span class="s2">&quot;gpu:0&quot;</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="n">dpnp_X_train</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">sycl_device</span><span class="p">)</span>
+<span class="n">dpnp_y_train</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">sycl_device</span><span class="p">)</span>
+<span class="n">dpnp_X_test</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">sycl_device</span><span class="p">)</span>
+
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dpnp_X_train</span><span class="p">,</span> <span class="n">dpnp_y_train</span><span class="p">)</span>
+
+<span class="n">pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">dpnp_X_test</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Random Forest regression results:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Ground truth (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">y_test</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Regression results (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Are predicted results on GPU: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">sycl_device</span><span class="o">.</span><span class="n">is_gpu</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Functional support doesn’t guarantee that after the model is trained, fitted attributes that are arrays
+will also be from the same namespace as the training data.</p>
+</div>
+<p>For example, if <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp’s</a> namespace was used for training,
+then fitted attributes will be on the CPU and <cite>numpy.ndarray</cite> data format.</p>
+</section>
+<section id="dpctl-usm-ndarrays">
+<h3>DPCTL usm_ndarrays<a class="headerlink" href="#dpctl-usm-ndarrays" title="Link to this heading"></a></h3>
+<p>Here is an example code to demonstrate how to use <a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a>
+arrays to run <cite>RandomForestClassifier</cite> on a GPU witout <cite>config_context(array_api_dispatch=True)</cite>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2023 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="c1"># sklearnex RF example for GPU offloading with DPCtl tensor:</span>
+<span class="c1">#    python ./random_forest_classifier_dpctl_batch.py</span>
+
+<span class="kn">import</span> <span class="nn">dpctl</span>
+<span class="kn">import</span> <span class="nn">dpctl.tensor</span> <span class="k">as</span> <span class="nn">dpt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+
+<span class="kn">from</span> <span class="nn">sklearnex.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
+
+<span class="c1"># Make sure that all DPCtl tensors using the same device.</span>
+<span class="n">q</span> <span class="o">=</span> <span class="n">dpctl</span><span class="o">.</span><span class="n">SyclQueue</span><span class="p">(</span><span class="s2">&quot;gpu&quot;</span><span class="p">)</span>  <span class="c1"># GPU</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+    <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="n">dpt_X_train</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">usm_type</span><span class="o">=</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">sycl_queue</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+<span class="n">dpt_y_train</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">usm_type</span><span class="o">=</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">sycl_queue</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+<span class="n">dpt_X_test</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">usm_type</span><span class="o">=</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">sycl_queue</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dpt_X_train</span><span class="p">,</span> <span class="n">dpt_y_train</span><span class="p">)</span>
+
+<span class="n">pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">dpt_X_test</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Random Forest classification results:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Ground truth (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">y_test</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Classification results (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Are predicted results on GPU: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">sycl_device</span><span class="o">.</span><span class="n">is_gpu</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>As on previous example, if <a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> Array API namespace was
+used for training, then fitted attributes will be on the CPU and <cite>numpy.ndarray</cite> data format.</p>
+</section>
+<section id="use-of-array-api-strict">
+<h3>Use of <cite>array-api-strict</cite><a class="headerlink" href="#use-of-array-api-strict" title="Link to this heading"></a></h3>
+<p>Here is an example code to demonstrate how to use <a class="reference external" href="https://github.com/data-apis/array-api-strict">array-api-strict</a>
+arrays to run <cite>DBSCAN</cite>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">array_api_strict</span>
+
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">config_context</span><span class="p">,</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">array_api_strict</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
+    <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.0</span><span class="p">,</span> <span class="mf">80.0</span><span class="p">]],</span>
+    <span class="n">dtype</span><span class="o">=</span><span class="n">array_api_strict</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Could be launched without `config_context(array_api_dispatch=True)`. This context</span>
+<span class="c1"># manager for sklearnex, only guarantee that in case of the fallback to stock</span>
+<span class="c1"># scikit-learn, fitted attributes to be from the same Array API namespace as</span>
+<span class="c1"># the training data.</span>
+<span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Fitted labels :</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">clustering</span><span class="o">.</span><span class="n">labels_</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+</section>
+
+
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+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
+          (function () {
+            var url = 'https://www.intel.com/content/dam/www/global/wap/tms-loader.js'; // WAP file URL
+            var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = url;
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+      </script>
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+</head>
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+<body class="wy-body-for-nav"> 
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+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/superior-machine-learning-performance-on-the-latest-intel-xeon-scalable-processor-efdec279f5a3">Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/leverage-intel-optimizations-in-scikit-learn-f562cb9d5544">Leverage Intel Optimizations in Scikit-Learn</a>,</p></li>
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+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae">From Hours to Minutes: 600x Faster SVM</a>,</p></li>
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+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a">Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit</a>,</p></li>
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+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe">Accelerate Linear Models for Machine Learning</a>,</p></li>
+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1">Accelerate K-Means Clustering</a>.</p></li>
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+      Choose version  <!-- this text may get changed later by javascript -->
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
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+             
+  <section id="how-to-contribute">
+<h1>How to Contribute<a class="headerlink" href="#how-to-contribute" title="Link to this heading"></a></h1>
+<p>As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn.
+This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.</p>
+<section id="licensing">
+<h2>Licensing<a class="headerlink" href="#licensing" title="Link to this heading"></a></h2>
+<p>Intel(R) Extension for Scikit-learn uses the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE">Apache 2.0 License</a>. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.</p>
+</section>
+<section id="pull-requests">
+<h2>Pull Requests<a class="headerlink" href="#pull-requests" title="Link to this heading"></a></h2>
+<p>No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.</p>
+<p>Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">name</span> <span class="s2">&quot;Kate Developer&quot;</span>
+<span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">email</span> <span class="n">kate</span><span class="o">.</span><span class="n">developer</span><span class="nd">@company</span><span class="o">.</span><span class="n">com</span>
+</pre></div>
+</div>
+<section id="before-contributing-changes">
+<h3>Before Contributing Changes<a class="headerlink" href="#before-contributing-changes" title="Link to this heading"></a></h3>
+<p>Make sure you can build the product and run all the tests with your patch.
+For a larger feature, provide a relevant test.
+Document your code. Our project uses reStructuredText for documentation.
+For new file(s), specify the appropriate copyright year in the first line.
+Submit a pull request into the master branch.
+Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.</p>
+</section>
+</section>
+<section id="code-style">
+<h2>Code Style<a class="headerlink" href="#code-style" title="Link to this heading"></a></h2>
+<p>We use <a class="reference external" href="https://black.readthedocs.io/en/stable/">black</a> and <a class="reference external" href="https://pycqa.github.io/isort/">isort</a> formatters for Python* code.
+The line length is 90 characters; use default options otherwise. You can find the linter configuration in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml">.pyproject.toml</a>.</p>
+<p>A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.</p>
+<p>Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pre</span><span class="o">-</span><span class="n">commit</span>
+<span class="n">pre</span><span class="o">-</span><span class="n">commit</span> <span class="n">install</span>
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+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li class="breadcrumb-item active">Distributed Mode</li>
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+            <a href="_sources/distributed-mode.rst.txt" rel="nofollow"> View page source</a>
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+  <section id="distributed-mode">
+<span id="distributed"></span><h1>Distributed Mode<a class="headerlink" href="#distributed-mode" title="Link to this heading"></a></h1>
+<p>Intel® Extension for Scikit-learn* offers Single Program, Multiple Data (SPMD) supported interfaces for distributed computing.
+Several <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/oneapi-gpu.html#">GPU-supported algorithms</a>
+also provide distributed, multi-GPU computing capabilities via integration with <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code>. The prerequisites
+match those of GPU computing, along with an MPI backend of your choice (<a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html#gs.dcan6r">Intel MPI recommended</a>, available
+via <code class="docutils literal notranslate"><span class="pre">impi-devel</span></code> python package) and the <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code> python package. If using Intel® Extension for Scikit-learn*
+<a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md#build-from-sources">installed from sources</a>,
+ensure that the spmd_backend is built.</p>
+<p>Note that Intel® Extension for Scikit-learn* now supports GPU offloading to speed up MPI operations. This is supported automatically with
+some MPI backends, but in order to use GPU offloading with Intel MPI, set the following environment variable (providing
+data on device without this may lead to a runtime error):</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">I_MPI_OFFLOAD</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+<p>Estimators can be imported from the <code class="docutils literal notranslate"><span class="pre">sklearnex.spmd</span></code> module. Data should be distributed across multiple nodes as
+desired, and should be transfered to a dpctl or dpnp array before being passed to the estimator. View a full
+example of this process in the Intel® Extension for Scikit-learn* repository, where many examples of our SPMD-supported estimators are
+available: <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/examples/sklearnex/">https://github.com/intel/scikit-learn-intelex/blob/main/examples/sklearnex/</a>. To run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mpirun</span> <span class="o">-</span><span class="n">n</span> <span class="mi">4</span> <span class="n">python</span> <span class="n">linear_regression_spmd</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+<p>Note that additional mpirun arguments can be added as desired. SPMD-supported estimators are listed in the
+<a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html#spmd-support">algorithms support documentation</a>.</p>
+<p>Additionally, daal4py offers some distributed functionality, see
+<a class="reference external" href="https://intelpython.github.io/daal4py/scaling.html">documentation</a> for further details.</p>
+</section>
+
+
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
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+        </div>
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+    </nav>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
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+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
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+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ 
+</div>
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics">BasicStatistics (class in sklearnex.basic_statistics)</a>
+</li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_">batch_size_ (sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_">(sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_">(sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_">coef_ (sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_">covariance_ (sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit">fit() (sklearnex.basic_statistics.BasicStatistics method)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit">(sklearnex.basic_statistics.IncrementalBasicStatistics method)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit">(sklearnex.covariance.IncrementalEmpiricalCovariance method)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit">(sklearnex.linear_model.IncrementalLinearRegression method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics">IncrementalBasicStatistics (class in sklearnex.basic_statistics)</a>
+</li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance">IncrementalEmpiricalCovariance (class in sklearnex.covariance)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression">IncrementalLinearRegression (class in sklearnex.linear_model)</a>
+</li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_">intercept_ (sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_">location_ (sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max">max (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean">mean (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min">min (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_">n_features_in_ (sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_">(sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_">(sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_">n_samples_seen_ (sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_">(sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_">(sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit">partial_fit() (sklearnex.basic_statistics.IncrementalBasicStatistics method)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit">(sklearnex.covariance.IncrementalEmpiricalCovariance method)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit">(sklearnex.linear_model.IncrementalLinearRegression method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict">predict() (sklearnex.linear_model.IncrementalLinearRegression method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment">second_order_raw_moment (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation">standard_deviation (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum">sum (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares">sum_squares (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered">sum_squares_centered (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance">variance (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation">variation (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+
+
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+            Intel(R) Extension for Scikit-learn*
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
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+
+
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+             
+  <section id="tuning-guide">
+<h1>Tuning Guide<a class="headerlink" href="#tuning-guide" title="Link to this heading"></a></h1>
+<p>The performance of some algorithms changes based on the parameters that are used.
+This section denotes the details of such cases.</p>
+<p>Refer to <a class="reference internal" href="../algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a> to see the full list of algorithms, parameters, and data formats supported in Intel® Extension for Scikit-learn*.</p>
+<section id="tsne">
+<span id="acceleration-tsne"></span><h2>TSNE<a class="headerlink" href="#tsne" title="Link to this heading"></a></h2>
+<p>TSNE algorithm consists of two components: KNN and Gradient Descent.
+The overall acceleration of TSNE depends on the acceleration of each of these algorithms.</p>
+<ul class="simple">
+<li><p>The KNN part of the algorithm supports all parameters except:</p>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</li>
+<li><p>The Gradient Descent part of the algorithm supports all parameters except:</p>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">n_components</span></code> = <cite>3</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">method</span></code> = <cite>‘exact’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">verbose</span></code> != <cite>0</cite></p></li>
+</ul>
+</li>
+</ul>
+<p>To get better performance, use parameters supported by both components.</p>
+</section>
+<section id="random-forest">
+<span id="acceleration-rf"></span><h2>Random Forest<a class="headerlink" href="#random-forest" title="Link to this heading"></a></h2>
+<p>Random Forest models accelerated with Intel® Extension for Scikit-learn* and using the <cite>hist</cite> splitting
+method discretize training data by creating a histogram with a configurable
+number of bins. The following keyword arguments can be used to influence the
+created histogram.</p>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 16.7%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Keyword argument</p></th>
+<th class="head"><p>Possible values</p></th>
+<th class="head"><p>Default value</p></th>
+<th class="head"><p>Description</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">maxBins</span></code></p></td>
+<td><p><cite>[0, inf)</cite></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">256</span></code></p></td>
+<td><p>Number of bins in the histogram with the discretized training data. The
+value <code class="docutils literal notranslate"><span class="pre">0</span></code> disables data discretization.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">minBinSize</span></code></p></td>
+<td><p><cite>[1, inf)</cite></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">5</span></code></p></td>
+<td><p>Minimum number of training data points in each bin after discretization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">binningStrategy</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">quantiles,</span> <span class="pre">averages</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">quantiles</span></code></p></td>
+<td><p>Selects the algorithm used to calculate bin edges. <code class="docutils literal notranslate"><span class="pre">quantiles</span></code>
+results in bins with a similar amount of training data points. <code class="docutils literal notranslate"><span class="pre">averages</span></code>
+divides the range of values observed in the training data set into
+equal-width bins of size <cite>(max - min) / maxBins</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+<p>Note that using discretized training data can greatly accelerate model training
+times, especially for larger data sets. However, due to the reduced fidelity of
+the data, the resulting model can present worse performance metrics compared to
+a model trained on the original data. In such cases, the number of bins can be
+increased with the <code class="docutils literal notranslate"><span class="pre">maxBins</span></code> parameter, or binning can be disabled entirely by
+setting <code class="docutils literal notranslate"><span class="pre">maxBins=0</span></code>.</p>
+</section>
+</section>
+
+
+           </div>
+          </div>
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+            Intel(R) Extension for Scikit-learn*
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
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+          <a href="#">Intel(R) Extension for Scikit-learn*</a>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn*</li>
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+            <a href="_sources/index.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="intelex">
+<span id="index"></span><h1>Intel® Extension for Scikit-learn*<a class="headerlink" href="#intelex" title="Link to this heading"></a></h1>
+<p>Intel(R) Extension for Scikit-learn is a <strong>free software AI accelerator</strong> designed to deliver up to <strong>100X</strong> faster performance for your existing scikit-learn code.
+The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.</p>
+<p class="rubric">Designed for Data Scientists and Framework Designers</p>
+<p>Use Intel(R) Extension for Scikit-learn, to:</p>
+<ul class="simple">
+<li><p>Speed up training and inference by up to 100x with the equivalent mathematical accuracy</p></li>
+<li><p>Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs</p></li>
+<li><p>Integrate the extension into your existing Scikit-learn applications without code modifications</p></li>
+<li><p>Enable and disable the extension with a couple of lines of code or at the command line</p></li>
+</ul>
+<p>Intel(R) Extension for Scikit-learn is also a part of <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html">Intel(R) AI Tools</a>.</p>
+<a class="reference internal image-reference" href="_images/scikit-learn-acceleration.PNG"><img alt="_images/scikit-learn-acceleration.PNG" src="_images/scikit-learn-acceleration.PNG" style="width: 800px;" />
+</a>
+<p>These performance charts use benchmarks that you can find in the <a class="reference external" href="https://github.com/IntelPython/scikit-learn_bench">scikit-learn bench repository</a>.</p>
+<section id="supported-algorithms">
+<h2>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Link to this heading"></a></h2>
+<p>See all of the <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a>.</p>
+</section>
+<section id="intel-r-optimizations">
+<h2>Intel(R) Optimizations<a class="headerlink" href="#intel-r-optimizations" title="Link to this heading"></a></h2>
+<section id="enable-intel-r-cpu-optimizations">
+<h3>Enable Intel(R) CPU Optimizations<a class="headerlink" href="#enable-intel-r-cpu-optimizations" title="Link to this heading"></a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="enable-intel-r-gpu-optimizations">
+<h3>Enable Intel(R) GPU optimizations<a class="headerlink" href="#enable-intel-r-gpu-optimizations" title="Link to this heading"></a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">dpctl</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span><span class="p">,</span> <span class="n">config_context</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">target_offload</span><span class="o">=</span><span class="s2">&quot;gpu:0&quot;</span><span class="p">):</span>
+    <span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
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+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
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+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
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+      <li class="breadcrumb-item active">Supported input types</li>
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+            <a href="_sources/input-types.rst.txt" rel="nofollow"> View page source</a>
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+  <section id="supported-input-types">
+<span id="input-types"></span><h1>Supported input types<a class="headerlink" href="#supported-input-types" title="Link to this heading"></a></h1>
+<p>Just like Scikit-Learn estimators, estimators from Intel(R)’s Extension are able to accept
+and work with different classes of input data, including:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://numpy.org/">Numpy</a> arrays.</p></li>
+<li><p>Other array classes implementing the <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API</a> protocol
+(see <a class="reference internal" href="array_api.html#array-api"><span class="std std-ref">Array API support</span></a> for details).</p></li>
+<li><p><a class="reference external" href="https://docs.scipy.org/doc/scipy/tutorial/sparse.html">SciPy sparse arrays and sparse matrices</a> (depending on the estimator).</p></li>
+<li><p><a class="reference external" href="https://pandas.pydata.org">Pandas</a> <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and <code class="docutils literal notranslate"><span class="pre">Series</span></code> classes.</p></li>
+</ul>
+<p>In addition, Intel(R)’s Extension also supports:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp</a> arrays.</p></li>
+<li><p><a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> arrays.</p></li>
+</ul>
+<p>Stock Scikit-Learn estimators, depending on the version, might offer support for additional
+input types beyond this list, such as <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and <code class="docutils literal notranslate"><span class="pre">Series</span></code> classes from other libraries
+like <a class="reference external" href="https://pola.rs">Polars</a>.</p>
+<p>Intel(R)’s Extension currently does not offer accelerated routines for input types not listed
+here - when receiving an unsupported class, estimators will fall back to stock Scikit-Learn to
+handle it, so make sure to convert them to a supported type when using Intel(R)’s Extension.</p>
+</section>
+
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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+  <section id="kaggle-kernels">
+<span id="kaggle"></span><h1>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Link to this heading"></a></h1>
+<p>See Kaggle notebooks created by Intel® Extension for Scikit-learn* developers.</p>
+<section id="acceleration">
+<h2>Acceleration<a class="headerlink" href="#acceleration" title="Link to this heading"></a></h2>
+<p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook">Introduction to scikit-learn-intelex</a>
+provides a summary of the speedup you can achieve with Intel® Extension for Scikit-learn*.</p>
+</section>
+<section id="machine-learning-workflows">
+<h2>Machine Learning Workflows<a class="headerlink" href="#machine-learning-workflows" title="Link to this heading"></a></h2>
+<p>Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.</p>
+<section id="kaggle-kernels-that-use-scikit-learn-and-intelex">
+<h3>Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-scikit-learn-and-intelex" title="Link to this heading"></a></h3>
+<div class="toctree-wrapper compound">
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+<li class="toctree-l1"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
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+</ul>
+</div>
+</section>
+<section id="kaggle-kernels-that-use-automl-with-intelex">
+<h3>Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-automl-with-intelex" title="Link to this heading"></a></h3>
+<div class="toctree-wrapper compound">
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+<li class="toctree-l1"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
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+          <li class="breadcrumb-item"><a href="../kaggle.html">Kaggle Kernels</a></li>
+      <li class="breadcrumb-item active">Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/kaggle/automl.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="kaggle-kernels-that-use-automl-and-intelex">
+<h1>Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*<a class="headerlink" href="#kaggle-kernels-that-use-automl-and-intelex" title="Link to this heading"></a></h1>
+<p>The following Kaggle kernels show how to patch autoML frameworks with Intel® Extension for Scikit-learn*.</p>
+<p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 66.7%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alex97andreev/tps-jun-autogluon-with-sklearnex">AutoML MultiClass Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Jun 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/lordozvlad/titanic-automl-with-intel-extension-for-sklearn/notebook">AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> Titanic datset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/lordozvlad/fast-automl-with-intel-extension-for-scikit-learn/notebook">AutoML Binary Classification (Gradient Boosting, Random Forest) using AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Oct 2021] Synthetic molecular response data</p>
+</td>
+<td><p>Predict the biological response of molecules given various chemical properties</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/lordozvlad/tps-nov-automl-with-intel-extension">AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using EvalML and AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Nov 2021] Synthetic spam emails data</p>
+</td>
+<td><p>Identify spam emails via features extracted from the email</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex/notebook">AutoML Binary Classification (Random Forest, SVR, Blending) using PyCaret with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Jan 2022] Fictional Sales data</p>
+</td>
+<td><p>Predict the corresponding item sales for each date-country-store-item combination</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
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+          </div>
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
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+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
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+          <li class="breadcrumb-item"><a href="../kaggle.html">Kaggle Kernels</a></li>
+      <li class="breadcrumb-item active">Kaggle Kernels for Classification Tasks</li>
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+             
+  <section id="kaggle-kernels-for-classification-tasks">
+<h1>Kaggle Kernels for Classification Tasks<a class="headerlink" href="#kaggle-kernels-for-classification-tasks" title="Link to this heading"></a></h1>
+<p>The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various classification tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.</p>
+<p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
+<section id="binary-classification">
+<h2>Binary Classification<a class="headerlink" href="#binary-classification" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-nov-log-regression-with-sklearnex-17x-speedup">Logistic Regression for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Nov 2021] Synthetic spam emails data</p>
+</td>
+<td><p>Identify spam emails via features extracted from the email</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing (normalization)</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/fast-feature-importance-using-scikit-learn-intelex/notebook">Feature Importance in Random Forest for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Nov 2021] Synthetic spam emails data</p>
+</td>
+<td><p>Identify spam emails via features extracted from the email</p></td>
+<td><ul class="simple">
+<li><p>reducing DataFrame memory usage</p></li>
+<li><p>computing feature importance with ELI5 and the default scikit-learn permutation importance</p></li>
+<li><p>training using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/andreyrus/tps-apr-rf-with-intel-extension-for-scikit-learn">Random Forest for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Apr 2021] Synthetic data based on Titanic dataset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature construction</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/tps04-svm-with-intel-extension-for-scikit-learn">Support Vector Classification (SVC) for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Apr 2021] Synthetic data based on Titanic dataset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/tps04-svm-with-scikit-learn-intelex">Support Vector Classification (SVC) with Feature Preprocessing for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Apr 2021] Synthetic data based on Titanic dataset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature engineering</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="multiclass-classification">
+<h2>MultiClass Classification<a class="headerlink" href="#multiclass-classification" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/tps-jun-fast-logreg-with-scikit-learn-intelex">Logistic Regression for MultiClass Classification with Quantile Transformer</a></p>
+<p><strong>Data:</strong> [TPS Jun 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing with Quantile Transformer</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex">Support Vector Classification (SVC) for MultiClass Classification (example 1)</a></p>
+<p><strong>Data:</strong> [TPS May 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/owerbat/tps-jun-fast-stacking-with-scikit-learn-intelex">Stacking Classifer with Logistic Regression, kNN, Random Forest, and Quantile Transformer</a></p>
+<p><strong>Data:</strong> [TPS Jun 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing: one-hot encoding, dimensionality reduction with PCA, normalization</p></li>
+<li><p>creating a stacking classifier with logistic regression, kNN, and random forest,
+and a pipeline of Quantile Transformer and another logistic regression as a final estimator</p></li>
+<li><p>searching for optimal parameters for the stacking classifier</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup">Support Vector Classification (SVC) for MultiClass Classification (example 2)</a></p>
+<p><strong>Data:</strong> [TPS Dec 2021] Synthetic Forest Cover Type data</p>
+</td>
+<td><p>Predict the forest cover type</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/tps-dec-fast-feature-importance-with-sklearnex">Feature Importance in Random Forest for MultiClass Classification</a></p>
+<p><strong>Data:</strong> [TPS Dec 2021] Synthetic Forest Cover Type data</p>
+</td>
+<td><p>Predict the forest cover type</p></td>
+<td><ul class="simple">
+<li><p>reducing DataFrame memory usage</p></li>
+<li><p>computing feature importance with ELI5</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/alexeykolobyanin/tps-feb-knn-with-sklearnex-13x-speedup">k-Nearest Neighbors (kNN) for MultiClass Classification</a></p>
+<p><strong>Data:</strong> [TPS Feb 2022] Bacteria DNA</p>
+</td>
+<td><p>Predict bacteria species based on repeated lossy measurements of DNA snippets</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="classification-tasks-in-computer-vision">
+<h2>Classification Tasks in Computer Vision<a class="headerlink" href="#classification-tasks-in-computer-vision" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-mnist?scriptVersionId=58739300">Support Vector Classification (SVC) for MultiClass Classification (CV task)</a></p>
+<p><strong>Data:</strong> Digit Recognizer (MNIST)</p>
+</td>
+<td><p>Recognize hand-written digits</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/fast-knn-using-scikit-learn-intelex-for-mnist?scriptVersionId=58738635">k-Nearest Neighbors (kNN) for MultiClass Classification (CV task)</a></p>
+<p><strong>Data:</strong> Digit Recognizer (MNIST)</p>
+</td>
+<td><p>Recognize hand-written digits</p></td>
+<td><ul class="simple">
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="classification-tasks-in-natural-language-processing">
+<h2>Classification Tasks in Natural Language Processing<a class="headerlink" href="#classification-tasks-in-natural-language-processing" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-nlp?scriptVersionId=58739339">Support Vector Classification (SVC) for a Binary Classification (NLP task)</a></p>
+<p><strong>Data:</strong> Natural Language Processing with Disaster Tweets</p>
+</td>
+<td><p>Predict which tweets are about real disasters and which ones are not</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>TF-IDF calculation</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/using-scikit-learn-intelex-for-what-s-cooking?scriptVersionId=58739642">One-vs-Rest Support Vector Machine (SVM) with Text Data for MultiClass Classification</a></p>
+<p><strong>Data:</strong> What’s Cooking</p>
+</td>
+<td><p>Use recipe ingredients to predict the cuisine</p></td>
+<td><ul class="simple">
+<li><p>feature extraction using TfidfVectorizer</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/code/alex97andreev/fast-svm-for-sparse-data-from-nlp-problem">Support Vector Classification (SVC) for Binary Classification with Sparse Data (NLP task)</a></p>
+<p><strong>Data:</strong> Stack Overflow questions</p>
+</td>
+<td><p>Predict the binary quality rating for Stack Overflow questions</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>TF-IDF calculation</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
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+  <section id="kaggle-kernels-for-regression-tasks">
+<h1>Kaggle Kernels for Regression Tasks<a class="headerlink" href="#kaggle-kernels-for-regression-tasks" title="Link to this heading"></a></h1>
+<p>The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various regression tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.</p>
+<p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
+<section id="using-a-single-regressor">
+<h2>Using a Single Regressor<a class="headerlink" href="#using-a-single-regressor" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/tps-jul-2021-baseline-with-nusvr-model/notebook">Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-aug-nusvr-with-intel-extension-for-sklearn">Nu Support Vector Regression (nuSVR)</a></p>
+<p><strong>Data:</strong> [TPS Aug 2021] Synthetic loan data</p>
+</td>
+<td><p>Calculate loss associated with a loan defaults</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature engineering</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/house-prices-nusvr-sklearn-intelex-4x-speedup">Nu Support Vector Regression (nuSVR)</a></p>
+<p><strong>Data:</strong> House Prices dataset</p>
+</td>
+<td><p>Predict sale prices for a property based on its characteristics</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>exploring outliers</p></li>
+<li><p>feature engineering</p></li>
+<li><p>filling missing values</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/pahandrovich/tps-jul-2021-fast-randomforest-with-sklearnex">Random Forest Regression</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>checking correlation between features</p></li>
+<li><p>search for best paramters using GridSearchCV</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-jul-rf-with-intel-extension-for-scikit-learn">Random Forest Regression with Feature Engineering</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature engineering</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/tps-mar-fast-workflow-using-scikit-learn-intelex">Random Forest Regression with Feature Importance Computation</a></p>
+<p><strong>Data:</strong> [TPS Mar 2022] Spatio-temporal traffic data</p>
+</td>
+<td><p>Forecast twelve-hours of traffic flow in a major U.S. metropolitan area</p></td>
+<td><ul class="simple">
+<li><p>feature engineering</p></li>
+<li><p>computing feature importance with ELI5</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-sep-ridge-with-sklearn-intelex-2x-speedup">Ridge Regression</a></p>
+<p><strong>Data:</strong> [TPS Sep 2021] Synthetic insurance data</p>
+</td>
+<td><p>Predict the probability of a customer making a claim upon an insurance policy</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>filling missing values</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="stacking-regressors">
+<h2>Stacking Regressors<a class="headerlink" href="#stacking-regressors" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-jul-stacking-with-scikit-learn-intelex">Stacking Regressor with Random Fores, SVR, and LASSO</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>feature engineering</p></li>
+<li><p>creating a stacking regressor</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/predict-sales-stacking-with-scikit-learn-intelex">Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data</a></p>
+<p><strong>Data:</strong> Predict Future Sales dataset</p>
+</td>
+<td><p>Predict total sales for every product and store in the next month based on daily sales data</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>creating a stacking regressor</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
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+</tr>
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+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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diff --git a/2025.0/non-scikit-algorithms.html b/2025.0/non-scikit-algorithms.html
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
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+  <section id="non-scikit-learn-algorithms">
+<h1>Non-Scikit-Learn Algorithms<a class="headerlink" href="#non-scikit-learn-algorithms" title="Link to this heading"></a></h1>
+<p>Algorithms not presented in the original scikit-learn are described here. All algorithms are
+available for both CPU and GPU (including distributed mode)</p>
+<section id="basicstatistics">
+<h2>BasicStatistics<a class="headerlink" href="#basicstatistics" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.basic_statistics.</span></span><span class="sig-name descname"><span class="pre">BasicStatistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/basic_statistics.html#BasicStatistics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics" title="Link to this definition"></a></dt>
+<dd><p>Estimator for basic statistics.
+Allows to compute basic statistics for provided data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>result_options</strong> (<em>string</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>, </em><em>default='all'</em>) – Used to set statistics to calculate. Possible values are <code class="docutils literal notranslate"><span class="pre">'min'</span></code>, <code class="docutils literal notranslate"><span class="pre">'max'</span></code>, <code class="docutils literal notranslate"><span class="pre">'sum'</span></code>, <code class="docutils literal notranslate"><span class="pre">'mean'</span></code>, <code class="docutils literal notranslate"><span class="pre">'variance'</span></code>,
+<code class="docutils literal notranslate"><span class="pre">'variation'</span></code>, <code class="docutils literal notranslate"><span class="pre">sum_squares'</span></code>, <code class="docutils literal notranslate"><span class="pre">sum_squares_centered'</span></code>, <code class="docutils literal notranslate"><span class="pre">'standard_deviation'</span></code>, <code class="docutils literal notranslate"><span class="pre">'second_order_raw_moment'</span></code>
+or a list containing any of these values. If set to <code class="docutils literal notranslate"><span class="pre">'all'</span></code> then all possible statistics will be
+calculated.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use in parallel for the computation.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means using all physical cores
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all logical cores.
+See <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-n_jobs" title="(in scikit-learn v1.6)"><span class="xref std std-term">Glossary</span></a> for more details.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.min">
+<span class="sig-name descname"><span class="pre">min</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.min" title="Link to this definition"></a></dt>
+<dd><p>Minimum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.max">
+<span class="sig-name descname"><span class="pre">max</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.max" title="Link to this definition"></a></dt>
+<dd><p>Maximum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.sum">
+<span class="sig-name descname"><span class="pre">sum</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.sum" title="Link to this definition"></a></dt>
+<dd><p>Sum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.mean">
+<span class="sig-name descname"><span class="pre">mean</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.mean" title="Link to this definition"></a></dt>
+<dd><p>Mean of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.variance">
+<span class="sig-name descname"><span class="pre">variance</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.variance" title="Link to this definition"></a></dt>
+<dd><p>Variance of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.variation">
+<span class="sig-name descname"><span class="pre">variation</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.variation" title="Link to this definition"></a></dt>
+<dd><p>Variation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.sum_squares">
+<span class="sig-name descname"><span class="pre">sum_squares</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares" title="Link to this definition"></a></dt>
+<dd><p>Sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.standard_deviation">
+<span class="sig-name descname"><span class="pre">standard_deviation</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.standard_deviation" title="Link to this definition"></a></dt>
+<dd><p>Standard deviation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.sum_squares_centered">
+<span class="sig-name descname"><span class="pre">sum_squares_centered</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered" title="Link to this definition"></a></dt>
+<dd><p>Centered sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment">
+<span class="sig-name descname"><span class="pre">second_order_raw_moment</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment" title="Link to this definition"></a></dt>
+<dd><p>Second order moment of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Attribute exists only if corresponding result option has been provided.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Some results can exhibit small variations due to
+floating point error accumulation and multithreading.</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.basic_statistics</span> <span class="kn">import</span> <span class="n">BasicStatistics</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span> <span class="o">=</span> <span class="n">BasicStatistics</span><span class="p">(</span><span class="n">result_options</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sum&#39;</span><span class="p">,</span> <span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span><span class="o">.</span><span class="n">sum_</span>
+<span class="go">np.array([4., 6.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span><span class="o">.</span><span class="n">min_</span>
+<span class="go">np.array([1., 2.])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.fit">
+<span class="sig-prename descclassname"><span class="pre">BasicStatistics.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/basic_statistics.html#BasicStatistics.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.fit" title="Link to this definition"></a></dt>
+<dd><p>Compute statistics with X, using minibatches of size batch_size.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Data for compute, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>sample_weight</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Weights for compute weighted statistics, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="incrementalbasicstatistics">
+<h2>IncrementalBasicStatistics<a class="headerlink" href="#incrementalbasicstatistics" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.basic_statistics.</span></span><span class="sig-name descname"><span class="pre">IncrementalBasicStatistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/incremental_basic_statistics.html#IncrementalBasicStatistics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics" title="Link to this definition"></a></dt>
+<dd><p>Calculates basic statistics on the given data, allows for computation when the data are split into
+batches. The user can use <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method to provide a single batch of data or use the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method to provide
+the entire dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>result_options</strong> (<em>string</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>, </em><em>default='all'</em>) – List of statistics to compute</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of samples to use for each batch. Only used when calling
+<code class="docutils literal notranslate"><span class="pre">fit</span></code>. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>
+is inferred from the data and set to <code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">*</span> <span class="pre">n_features</span></code>.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use in parallel for the computation.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means using all physical cores
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all logical cores.
+See <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-n_jobs" title="(in scikit-learn v1.6)"><span class="xref std std-term">Glossary</span></a> for more details.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.min">
+<span class="sig-name descname"><span class="pre">min</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.min" title="Link to this definition"></a></dt>
+<dd><p>Minimum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.max">
+<span class="sig-name descname"><span class="pre">max</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.max" title="Link to this definition"></a></dt>
+<dd><p>Maximum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.sum">
+<span class="sig-name descname"><span class="pre">sum</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum" title="Link to this definition"></a></dt>
+<dd><p>Sum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.mean">
+<span class="sig-name descname"><span class="pre">mean</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.mean" title="Link to this definition"></a></dt>
+<dd><p>Mean of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.variance">
+<span class="sig-name descname"><span class="pre">variance</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variance" title="Link to this definition"></a></dt>
+<dd><p>Variance of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.variation">
+<span class="sig-name descname"><span class="pre">variation</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variation" title="Link to this definition"></a></dt>
+<dd><p>Variation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares">
+<span class="sig-name descname"><span class="pre">sum_squares</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares" title="Link to this definition"></a></dt>
+<dd><p>Sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation">
+<span class="sig-name descname"><span class="pre">standard_deviation</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation" title="Link to this definition"></a></dt>
+<dd><p>Standard deviation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered">
+<span class="sig-name descname"><span class="pre">sum_squares_centered</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered" title="Link to this definition"></a></dt>
+<dd><p>Centered sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment">
+<span class="sig-name descname"><span class="pre">second_order_raw_moment</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment" title="Link to this definition"></a></dt>
+<dd><p>Second order moment of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_">
+<span class="sig-name descname"><span class="pre">n_samples_seen_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_" title="Link to this definition"></a></dt>
+<dd><p>The number of samples processed by the estimator. Will be reset on
+new calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>, but increments across <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> calls.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_">
+<span class="sig-name descname"><span class="pre">batch_size_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_" title="Link to this definition"></a></dt>
+<dd><p>Inferred batch size from <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_">
+<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_" title="Link to this definition"></a></dt>
+<dd><p>Number of features seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> or  <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Attribute exists only if corresponding result option has been provided.</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.basic_statistics</span> <span class="kn">import</span> <span class="n">IncrementalBasicStatistics</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span> <span class="o">=</span> <span class="n">IncrementalBasicStatistics</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">sum_</span>
+<span class="go">np.array([4., 6.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">min_</span>
+<span class="go">np.array([1., 2.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">sum_</span>
+<span class="go">np.array([4., 6.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">max_</span>
+<span class="go">np.array([3., 4.])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalBasicStatistics.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/incremental_basic_statistics.html#IncrementalBasicStatistics.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.fit" title="Link to this definition"></a></dt>
+<dd><p>Compute statistics with X, using minibatches of size batch_size.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Data for compute, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>sample_weight</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Weights for compute weighted statistics, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalBasicStatistics.</span></span><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/incremental_basic_statistics.html#IncrementalBasicStatistics.partial_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit" title="Link to this definition"></a></dt>
+<dd><p>Incremental fit with X. All of X is processed as a single batch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Data for compute, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>sample_weight</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Weights for compute weighted statistics, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="incrementalempiricalcovariance">
+<h2>IncrementalEmpiricalCovariance<a class="headerlink" href="#incrementalempiricalcovariance" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.covariance.</span></span><span class="sig-name descname"><span class="pre">IncrementalEmpiricalCovariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/covariance/incremental_covariance.html#IncrementalEmpiricalCovariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance" title="Link to this definition"></a></dt>
+<dd><p>Maximum likelihood covariance estimator that allows for the estimation when the data are split into
+batches. The user can use the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method to provide a single batch of data or use the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method to provide
+the entire dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>store_precision</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=False</em>) – Specifies if the estimated precision is stored.</p></li>
+<li><p><strong>assume_centered</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=False</em>) – If True, data are not centered before computation.
+Useful when working with data whose mean is almost, but not exactly
+zero.
+If False (default), data are centered before computation.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of samples to use for each batch. Only used when calling
+<code class="docutils literal notranslate"><span class="pre">fit</span></code>. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>
+is inferred from the data and set to <code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">*</span> <span class="pre">n_features</span></code>, to provide a
+balance between approximation accuracy and memory consumption.</p></li>
+<li><p><strong>copy</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – If False, X will be overwritten. <code class="docutils literal notranslate"><span class="pre">copy=False</span></code> can be used to
+save memory but is unsafe for general use.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use in parallel for the computation.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means using all physical cores
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all logical cores.
+See <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-n_jobs" title="(in scikit-learn v1.6)"><span class="xref std std-term">Glossary</span></a> for more details.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.location_">
+<span class="sig-name descname"><span class="pre">location_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.location_" title="Link to this definition"></a></dt>
+<dd><p>Estimated location, i.e. the estimated mean.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_">
+<span class="sig-name descname"><span class="pre">covariance_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_" title="Link to this definition"></a></dt>
+<dd><p>Estimated covariance matrix</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features, n_features)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_">
+<span class="sig-name descname"><span class="pre">n_samples_seen_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_" title="Link to this definition"></a></dt>
+<dd><p>The number of samples processed by the estimator. Will be reset on
+new calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>, but increments across <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> calls.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_">
+<span class="sig-name descname"><span class="pre">batch_size_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_" title="Link to this definition"></a></dt>
+<dd><p>Inferred batch size from <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_">
+<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_" title="Link to this definition"></a></dt>
+<dd><p>Number of features seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.covariance</span> <span class="kn">import</span> <span class="n">IncrementalEmpiricalCovariance</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span> <span class="o">=</span> <span class="n">IncrementalEmpiricalCovariance</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">covariance_</span>
+<span class="go">np.array([[1., 1.],[1., 1.]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">location_</span>
+<span class="go">np.array([2., 3.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">covariance_</span>
+<span class="go">np.array([[1., 1.],[1., 1.]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">location_</span>
+<span class="go">np.array([2., 3.])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalEmpiricalCovariance.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/covariance/incremental_covariance.html#IncrementalEmpiricalCovariance.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.fit" title="Link to this definition"></a></dt>
+<dd><p>Fit the model with X, using minibatches of size batch_size.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <cite>n_samples</cite> is the number of samples and
+<cite>n_features</cite> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalEmpiricalCovariance.</span></span><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/covariance/incremental_covariance.html#IncrementalEmpiricalCovariance.partial_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit" title="Link to this definition"></a></dt>
+<dd><p>Incremental fit with X. All of X is processed as a single batch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <cite>n_samples</cite> is the number of samples and
+<cite>n_features</cite> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>check_input</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – Run check_array on X.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="incrementallinearregression">
+<h2>IncrementalLinearRegression<a class="headerlink" href="#incrementallinearregression" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.linear_model.</span></span><span class="sig-name descname"><span class="pre">IncrementalLinearRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression" title="Link to this definition"></a></dt>
+<dd><p>Trains a linear regression model, allows for computation if the data are split into
+batches. The user can use the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method to provide a single batch of data or use the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method to provide
+the entire dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fit_intercept</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>)</p></li>
+<li><p><strong>set</strong> (<em>Whether to calculate the intercept for this model. If</em>)</p></li>
+<li><p><strong>False</strong> (<em>to</em>)</p></li>
+<li><p><strong>calculations</strong> (<em>no intercept will be used in</em>)</p></li>
+<li><p><strong>centered</strong><strong>)</strong><strong>.</strong> (<em>(</em><em>i.e. data is expected to be</em>)</p></li>
+<li><p><strong>copy_X</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – If True, X will be copied; else, it may be overwritten.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use for the computation.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of samples to use for each batch. Only used when calling
+<code class="docutils literal notranslate"><span class="pre">fit</span></code>. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>
+is inferred from the data and set to <code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">*</span> <span class="pre">n_features</span></code>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.coef_">
+<span class="sig-name descname"><span class="pre">coef_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.coef_" title="Link to this definition"></a></dt>
+<dd><p>Estimated coefficients for the linear regression problem.
+If multiple targets are passed during the fit (y 2D), this
+is a 2D array of shape (n_targets, n_features), while if only
+one target is passed, this is a 1D array of length n_features.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array of shape (n_features, ) or (n_targets, n_features)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.intercept_">
+<span class="sig-name descname"><span class="pre">intercept_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.intercept_" title="Link to this definition"></a></dt>
+<dd><p>Independent term in the linear model. Set to 0.0 if
+<cite>fit_intercept = False</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a> or array of shape (n_targets,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_">
+<span class="sig-name descname"><span class="pre">n_samples_seen_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_" title="Link to this definition"></a></dt>
+<dd><p>The number of samples processed by the estimator. Will be reset on
+new calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>, but increments across <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> calls.
+It should be not less than <cite>n_features_in_</cite> if <cite>fit_intercept</cite>
+is False and not less than <cite>n_features_in_</cite> + 1 if <cite>fit_intercept</cite>
+is True to obtain regression coefficients.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.batch_size_">
+<span class="sig-name descname"><span class="pre">batch_size_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.batch_size_" title="Link to this definition"></a></dt>
+<dd><p>Inferred batch size from <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.n_features_in_">
+<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_" title="Link to this definition"></a></dt>
+<dd><p>Number of features seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.linear_model</span> <span class="kn">import</span> <span class="n">IncrementalLinearRegression</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span> <span class="o">=</span> <span class="n">IncrementalLinearRegression</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">10</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">,</span> <span class="mf">8.5</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span> <span class="n">y</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span> <span class="n">y</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">coef_</span>
+<span class="go">np.array([0.5., 0.5.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">intercept_</span>
+<span class="go">np.array(0.)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">coef_</span>
+<span class="go">np.array([0.5., 0.5.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">intercept_</span>
+<span class="go">np.array(0.)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalLinearRegression.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.fit" title="Link to this definition"></a></dt>
+<dd><p>Fit the model with X and y, using minibatches of size <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features. It is necessary for
+<code class="docutils literal notranslate"><span class="pre">n_samples</span></code> to be not less than <code class="docutils literal notranslate"><span class="pre">n_features</span></code> if <code class="docutils literal notranslate"><span class="pre">fit_intercept</span></code>
+is False and not less than <code class="docutils literal notranslate"><span class="pre">n_features</span> <span class="pre">+</span> <span class="pre">1</span></code> if <code class="docutils literal notranslate"><span class="pre">fit_intercept</span></code>
+is True</p></li>
+<li><p><strong>y</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>) or </em><em>(</em><em>n_samples</em><em>, </em><em>n_targets</em><em>)</em>) – Target values, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_targets</span></code> is the number of targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.partial_fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalLinearRegression.</span></span><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression.partial_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.partial_fit" title="Link to this definition"></a></dt>
+<dd><p>Incremental fit linear model with X and y. All of X and y is
+processed as a single batch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<cite>n_features</cite> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>) or </em><em>(</em><em>n_samples</em><em>, </em><em>n_targets</em><em>)</em>) – Target values, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_targets</span></code> is the number of targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.predict">
+<span class="sig-prename descclassname"><span class="pre">IncrementalLinearRegression.</span></span><span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.predict" title="Link to this definition"></a></dt>
+<dd><p>Predict using the linear model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> or </em><em>sparse matrix</em><em>, </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Samples.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>C</strong> – Returns predicted values.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array, shape (n_samples, n_targets)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
+
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+
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
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+            Intel(R) Extension for Scikit-learn*
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+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
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+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
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+  <section id="oneapi-and-gpu-support-in-intelex">
+<span id="oneapi-gpu"></span><h1>oneAPI and GPU support in Intel® Extension for Scikit-learn*<a class="headerlink" href="#oneapi-and-gpu-support-in-intelex" title="Link to this heading"></a></h1>
+<p>Intel® Extension for Scikit-learn* supports oneAPI concepts, which
+means that algorithms can be executed on different devices: CPUs and GPUs.
+This is done via integration with
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> package that
+implements core oneAPI concepts like queues and devices.</p>
+<section id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading"></a></h2>
+<p>For execution on GPU, DPC++ compiler runtime and driver are required. Refer to <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html">DPC++ system
+requirements</a> for details.</p>
+<p>DPC++ compiler runtime can be installed either from PyPI or Anaconda:</p>
+<ul>
+<li><p>Install from PyPI:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">dpcpp</span><span class="o">-</span><span class="n">cpp</span><span class="o">-</span><span class="n">rt</span>
+</pre></div>
+</div>
+</li>
+<li><p>Install using Conda via the Intel repository:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">dpcpp_cpp_rt</span> <span class="o">-</span><span class="n">c</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">software</span><span class="o">.</span><span class="n">repos</span><span class="o">.</span><span class="n">intel</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">python</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span>
+</pre></div>
+</div>
+</li>
+</ul>
+</section>
+<section id="device-offloading">
+<h2>Device offloading<a class="headerlink" href="#device-offloading" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn* offers two options for running an algorithm on a
+specific device with the help of dpctl:</p>
+<ul>
+<li><p>Pass input data as <a class="reference external" href="https://intelpython.github.io/dpctl/latest/docfiles/dpctl/usm_ndarray.html#dpctl.tensor.usm_ndarray">dpctl.tensor.usm_ndarray</a> to the algorithm.</p>
+<p>The computation will run on the device where the input data is
+located, and the result will be returned as <code class="code docutils literal notranslate"><span class="pre">usm_ndarray</span></code> to the same
+device.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>All the input data for an algorithm must reside on the same device.</p>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The <code class="code docutils literal notranslate"><span class="pre">usm_ndarray</span></code> can only be consumed by the base methods
+like <code class="code docutils literal notranslate"><span class="pre">fit</span></code>, <code class="code docutils literal notranslate"><span class="pre">predict</span></code>, and <code class="code docutils literal notranslate"><span class="pre">transform</span></code>.
+Note that only the algorithms in Intel® Extension for Scikit-learn* support
+<code class="code docutils literal notranslate"><span class="pre">usm_ndarray</span></code>. The algorithms from the stock version of scikit-learn
+do not support this feature.</p>
+</div>
+</li>
+<li><p>Use global configurations of Intel® Extension for Scikit-learn**:</p>
+<ol class="arabic simple">
+<li><p>The <code class="code docutils literal notranslate"><span class="pre">target_offload</span></code> option can be used to set the device primarily
+used to perform computations. Accepted data types are <code class="code docutils literal notranslate"><span class="pre">str</span></code> and
+<code class="code docutils literal notranslate"><span class="pre">dpctl.SyclQueue</span></code>. If you pass a string to <code class="code docutils literal notranslate"><span class="pre">target_offload</span></code>,
+it should either be <code class="docutils literal notranslate"><span class="pre">&quot;auto&quot;</span></code>, which means that the execution
+context is deduced from the location of input data, or a string
+with SYCL* filter selector. The default value is <code class="docutils literal notranslate"><span class="pre">&quot;auto&quot;</span></code>.</p></li>
+<li><p>The <code class="code docutils literal notranslate"><span class="pre">allow_fallback_to_host</span></code> option
+is a Boolean flag. If set to <code class="code docutils literal notranslate"><span class="pre">True</span></code>, the computation is allowed
+to fallback to the host device when a particular estimator does not support
+the selected device. The default value is <code class="code docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+</ol>
+</li>
+</ul>
+<p>These options can be set using <code class="code docutils literal notranslate"><span class="pre">sklearnex.set_config()</span></code> function or
+<code class="code docutils literal notranslate"><span class="pre">sklearnex.config_context</span></code>. To obtain the current values of these options,
+call <code class="code docutils literal notranslate"><span class="pre">sklearnex.get_config()</span></code>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Functions <code class="code docutils literal notranslate"><span class="pre">set_config</span></code>, <code class="code docutils literal notranslate"><span class="pre">get_config</span></code> and <code class="code docutils literal notranslate"><span class="pre">config_context</span></code>
+are always patched after the <code class="code docutils literal notranslate"><span class="pre">sklearnex.patch_sklearn()</span></code> call.</p>
+</div>
+<p class="rubric">Compatibility considerations</p>
+<p>For compatibility reasons, algorithms in Intel® Extension for Scikit-learn* may be offloaded to the device using
+<code class="code docutils literal notranslate"><span class="pre">daal4py.oneapi.sycl_context</span></code>. However, it is recommended to use one of the options
+described above for device offloading instead of using <code class="code docutils literal notranslate"><span class="pre">sycl_context</span></code>.</p>
+</section>
+<section id="example">
+<h2>Example<a class="headerlink" href="#example" title="Link to this heading"></a></h2>
+<p>An example on how to patch your code with Intel CPU/GPU optimizations:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span><span class="p">,</span> <span class="n">config_context</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">target_offload</span><span class="o">=</span><span class="s2">&quot;gpu:0&quot;</span><span class="p">):</span>
+   <span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Current offloading behavior restricts fitting and inference of any models to be
+in the same context or absence of context. For example, a model trained in the GPU context with
+target_offload=”gpu:0” throws an error if the inference is made outside the same GPU context.</p>
+</div>
+</section>
+</section>
+
+
+           </div>
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+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Preview Functionality</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="preview-functionality">
+<span id="preview"></span><h1>Preview Functionality<a class="headerlink" href="#preview-functionality" title="Link to this heading"></a></h1>
+<p>Some of implemented functionality in Intel® Extension for Scikit-learn* doesn’t meet one or few of next requirements
+for being enabled by default for all users:</p>
+<ul class="simple">
+<li><p>The functionality API is not stable and can be changed in future</p></li>
+<li><p>The functionality doesn’t have full compatibility with stock Scikit-learn</p></li>
+<li><p>The functionality misses performance targets compared to stock Scikit-learn or previously available version of functionality</p></li>
+<li><p>The functionality is not fully tested</p></li>
+</ul>
+<p>This type of functionality is available under <strong>preview mode</strong> of Intel® Extension for Scikit-learn* and located in
+the corresponding module (<cite>sklearnex.preview</cite>).</p>
+<p>Preview functionality <em>may</em> or <em>may not</em> participate in patching of Scikit-learn.
+For example, a preview estimator may be a replacement for a stock one or a completely new one.</p>
+<p>To enable preview functionality, you need to set the <cite>SKLEARNEX_PREVIEW</cite> environment variable
+to non-empty value before patching of Scikit-learn.
+For example, you can set the environment variable in the following way:</p>
+<ul>
+<li><p>On Linux* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">SKLEARNEX_PREVIEW</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+</li>
+<li><p>On Windows* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span> <span class="n">SKLEARNEX_PREVIEW</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+</li>
+</ul>
+<p>Then, you can import Scikit-learn estimator patched with a preview one from <cite>sklearnex.preview</cite> module:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">IncrementalPCA</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">IncrementalPCA</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span>
+<span class="c1"># output:</span>
+<span class="c1"># sklearnex.preview.decomposition.incremental_pca</span>
+</pre></div>
+</div>
+<p>Current list of preview estimators:</p>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+<col style="width: 16.7%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Estimator name</p></th>
+<th class="head"><p>Module</p></th>
+<th class="head"><p>Is patching supported</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>EmpiricalCovariance</p></td>
+<td><p>sklearnex.preview.covariance</p></td>
+<td><p>Yes</p></td>
+</tr>
+<tr class="row-odd"><td><p>IncrementalPCA</p></td>
+<td><p>sklearnex.preview.decomposition</p></td>
+<td><p>Yes</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
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+      <li class="breadcrumb-item active">Quick Start</li>
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+            <a href="_sources/quick-start.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="quick-start">
+<h1>Quick Start<a class="headerlink" href="#quick-start" title="Link to this heading"></a></h1>
+<p>Get ready to elevate your scikit-learn code with Intel® Extension for Scikit-learn* and experience the benefits of accelerated performance in just a few simple steps.</p>
+<section id="compatibility-with-scikit-learn">
+<h2>Compatibility with Scikit-learn*<a class="headerlink" href="#compatibility-with-scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.</p>
+</section>
+<section id="integrate-intelex">
+<h2>Integrate Intel® Extension for Scikit-learn*<a class="headerlink" href="#integrate-intelex" title="Link to this heading"></a></h2>
+<section id="patching">
+<h3>Patching<a class="headerlink" href="#patching" title="Link to this heading"></a></h3>
+<p>Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension.
+This action is called <code class="docutils literal notranslate"><span class="pre">patching</span></code>. This is not a permanent change so you can always undo the patching if necessary.</p>
+<p>To patch Intel® Extension for Scikit-learn, use one of these methods:</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Method</p></th>
+<th class="head"><p>Action</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Use a flag in the command line</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span> <span class="n">my_application</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+</td>
+</tr>
+<tr class="row-odd"><td><p>Modify your script</p></td>
+<td><p>Add the following lines:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</td>
+</tr>
+<tr class="row-even"><td><p>Import an estimator from the <code class="docutils literal notranslate"><span class="pre">sklearnex</span></code> module</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
+</pre></div>
+</div>
+</td>
+</tr>
+</tbody>
+</table>
+<p>These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.</p>
+<p><strong>Example</strong></p>
+<p>This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.</p>
+<div class="literal-block-wrapper docutils container" id="id2">
+<div class="code-block-caption"><span class="caption-text">Example: Drop-In Patching</span><a class="headerlink" href="#id2" title="Link to this code"></a></div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>  <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+  <span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+  <span class="n">patch_sklearn</span><span class="p">()</span>
+
+  <span class="c1"># You need to re-import scikit-learn algorithms after the patch</span>
+  <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+  <span class="c1"># The use of the original Scikit-learn is not changed</span>
+  <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">0</span><span class="p">],</span>
+              <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
+  <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+  <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;kmeans.labels_ = </span><span class="si">{</span><span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="global-patching">
+<h3>Global Patching<a class="headerlink" href="#global-patching" title="Link to this heading"></a></h3>
+<p>You can also use global patching to patch all your scikit-learn applications without any additional actions.</p>
+<p>Before you begin, make sure that you have read and write permissions for Scikit-learn files.</p>
+<p>With global patching, you can:</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Task</p></th>
+<th class="head"><p>Action</p></th>
+<th class="head"><p>Note</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Patch all supported algorithms</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
+</pre></div>
+</div>
+</td>
+<td><p>If you run the global patching command several times with different parameters, then only the last configuration is applied.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Patch selected algorithms</p></td>
+<td><p>Use <code class="docutils literal notranslate"><span class="pre">--algorithm</span></code> or <code class="docutils literal notranslate"><span class="pre">-a</span></code> keys with a list of algorithms to patch. For example, to patch only <code class="docutils literal notranslate"><span class="pre">SVC</span></code> and <code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> estimators, run</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>Enable global patching via code</p></td>
+<td><p>Use the <code class="docutils literal notranslate"><span class="pre">patch_sklearn</span></code> function with the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">sklearn</span>
+</pre></div>
+</div>
+</td>
+<td><p>After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Disable patching notifications</p></td>
+<td><p>Use <code class="docutils literal notranslate"><span class="pre">--no-verbose</span></code> or <code class="docutils literal notranslate"><span class="pre">-nv</span></code> keys:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span> <span class="o">-</span><span class="n">nv</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>Disable global patching</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">unpatch_sklearn</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p>Disable global patching via code</p></td>
+<td><p>Use the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument in the <code class="docutils literal notranslate"><span class="pre">unpatch_sklearn</span></code> function</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+<span class="n">unpatch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>If you clone an environment with enabled global patching, it will already be applied in the new environment.</p>
+</div>
+</section>
+<section id="unpatching">
+<h3>Unpatching<a class="headerlink" href="#unpatching" title="Link to this heading"></a></h3>
+<p>To undo the patch (also called <cite>unpatching</cite>) is to return scikit-learn to original implementation and
+replace patched algorithms with the stock scikit-learn algorithms.</p>
+<p>To unpatch successfully, you must reimport the scikit-learn package:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sklearnex</span><span class="o">.</span><span class="n">unpatch_sklearn</span><span class="p">()</span>
+<span class="c1"># Re-import scikit-learn algorithms after the unpatch</span>
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading"></a></h2>
+<nav class="contents local" id="contents">
+<ul class="simple">
+<li><p><a class="reference internal" href="#install-from-pypi" id="id6">Install from PyPI</a></p></li>
+<li><p><a class="reference internal" href="#install-from-anaconda-cloud" id="id7">Install from Anaconda* Cloud</a></p></li>
+<li><p><a class="reference internal" href="#build-from-sources" id="id8">Build from Sources</a></p></li>
+<li><p><a class="reference internal" href="#install-intel-r-ai-tools" id="id9">Install Intel*(R) AI Tools</a></p></li>
+</ul>
+</nav>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>To prevent version conflicts, we recommend creating and activating a new environment for Intel® Extension for Scikit-learn*.</p>
+</div>
+<section id="install-from-pypi">
+<h3><a class="toc-backref" href="#id6" role="doc-backlink">Install from PyPI</a><a class="headerlink" href="#install-from-pypi" title="Link to this heading"></a></h3>
+<p>Recommended by default.</p>
+<p>To install Intel® Extension for Scikit-learn*, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
+</pre></div>
+</div>
+<p><strong>Supported Configurations</strong></p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="install-from-anaconda-cloud">
+<h3><a class="toc-backref" href="#id7" role="doc-backlink">Install from Anaconda* Cloud</a><a class="headerlink" href="#install-from-anaconda-cloud" title="Link to this heading"></a></h3>
+<p>To prevent version conflicts, we recommend installing <cite>scikit-learn-intelex</cite> into a new conda environment.</p>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-0-0-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-0-0-0" name="0-0" role="tab" tabindex="0">Conda-Forge channel</button><button aria-controls="panel-0-0-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-1" name="0-1" role="tab" tabindex="-1">Intel channel</button><button aria-controls="panel-0-0-2" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-2" name="0-2" role="tab" tabindex="-1">Main channel</button></div><div aria-labelledby="tab-0-0-0" class="sphinx-tabs-panel" id="panel-0-0-0" name="0-0" role="tabpanel" tabindex="0"><p>Recommended by default.</p>
+<p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id3">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id3" title="Link to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div><div aria-labelledby="tab-0-0-1" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-1" name="0-1" role="tabpanel" tabindex="0"><p>Recommended for the Intel® Distribution for Python users.</p>
+<p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">software</span><span class="o">.</span><span class="n">repos</span><span class="o">.</span><span class="n">intel</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">python</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id4">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id4" title="Link to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div><div aria-labelledby="tab-0-0-2" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-2" name="0-2" role="tabpanel" tabindex="0"><p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id5">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id5" title="Link to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div></div>
+</section>
+<section id="build-from-sources">
+<h3><a class="toc-backref" href="#id8" role="doc-backlink">Build from Sources</a><a class="headerlink" href="#build-from-sources" title="Link to this heading"></a></h3>
+<p>See <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md">Installation instructions</a> to build Intel® Extension for Scikit-learn* from the sources.</p>
+</section>
+<section id="install-intel-r-ai-tools">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Install Intel*(R) AI Tools</a><a class="headerlink" href="#install-intel-r-ai-tools" title="Link to this heading"></a></h3>
+<p>Download the Intel AI Tools <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html">here</a>. The extension is already included.</p>
+</section>
+</section>
+<section id="release-notes">
+<h2>Release Notes<a class="headerlink" href="#release-notes" title="Link to this heading"></a></h2>
+<p>See the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/releases">Release Notes</a> for each version of Intel® Extension for Scikit-learn*.</p>
+</section>
+<section id="system-requirements">
+<h2>System Requirements<a class="headerlink" href="#system-requirements" title="Link to this heading"></a></h2>
+<section id="hardware-requirements">
+<h3>Hardware Requirements<a class="headerlink" href="#hardware-requirements" title="Link to this heading"></a></h3>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-1-1-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-1-1-0" name="1-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-1-1-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-1-1-1" name="1-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-1-1-0" class="sphinx-tabs-panel" id="panel-1-1-0" name="1-0" role="tabpanel" tabindex="0"><p>All processors with <code class="docutils literal notranslate"><span class="pre">x86</span></code> architecture with at least one of the following instruction sets:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>SSE2</p></li>
+<li><p>SSE4.2</p></li>
+<li><p>AVX2</p></li>
+<li><p>AVX512</p></li>
+</ul>
+</div></blockquote>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>ARM* architecture is not supported.</p>
+</div>
+</div><div aria-labelledby="tab-1-1-1" class="sphinx-tabs-panel" hidden="true" id="panel-1-1-1" name="1-1" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>All Intel® integrated and discrete GPUs</p></li>
+<li><p>Intel® GPU drivers</p></li>
+</ul>
+</div></div>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our <a class="reference internal" href="blogs.html#blogs"><span class="std std-ref">blogs</span></a>.</p>
+</div>
+</section>
+<section id="software-requirements">
+<h3>Software Requirements<a class="headerlink" href="#software-requirements" title="Link to this heading"></a></h3>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-2-2-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-2-2-0" name="2-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-2-2-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-2-2-1" name="2-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-2-2-0" class="sphinx-tabs-panel" id="panel-2-2-0" name="2-0" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
+<li><p>Windows* OS 10 or newer</p></li>
+<li><p>Windows* Server 2019 or newer</p></li>
+</ul>
+</div><div aria-labelledby="tab-2-2-1" class="sphinx-tabs-panel" hidden="true" id="panel-2-2-1" name="2-1" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
+<li><p>Windows* OS 10 or newer</p></li>
+<li><p>Windows* Server 2019 or newer</p></li>
+</ul>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<p>If you use accelerators, refer to <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html">oneAPI DPC++/C++ Compiler System Requirements</a>.</p>
+</div>
+</div></div>
+<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:</p>
+<ul class="simple">
+<li><p>1.0.X</p></li>
+<li><p>1.1.X</p></li>
+<li><p>1.2.X</p></li>
+<li><p>1.3.X</p></li>
+</ul>
+</section>
+<section id="memory-requirements">
+<h3>Memory Requirements<a class="headerlink" href="#memory-requirements" title="Link to this heading"></a></h3>
+<p>By default, algorithms in Intel® Extension for Scikit-learn* run in the multi-thread mode. This mode uses all available threads.
+Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Single-thread mode</p></th>
+<th class="head"><p>Multi-thread mode</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>SVM</p></td>
+<td><p>Both Scikit-learn and Intel® Extension for Scikit-learn* consume approximately the same amount of RAM.</p></td>
+<td><p>In Intel® Extension for Scikit-learn*, an algorithm with <code class="docutils literal notranslate"><span class="pre">N</span></code> threads consumes <code class="docutils literal notranslate"><span class="pre">N</span></code> times more RAM.</p></td>
+</tr>
+</tbody>
+</table>
+<p>In all Intel® Extension for Scikit-learn* algorithms with GPU support, computations run on device memory.
+The device memory must be large enough to store a copy of the entire dataset.
+You may also require additional device memory for internal arrays that are used in computation.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="samples.html#samples"><span class="std std-ref">Samples</span></a></p>
+</div>
+</section>
+</section>
+</section>
+
+
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="next" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="samples/knn_mnist.html" />
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+  <section id="samples">
+<span id="id1"></span><h1>Samples<a class="headerlink" href="#samples" title="Link to this heading"></a></h1>
+<p>The following samples are also provided as Jupyter notebooks in Intel® Extension for Scikit-learn* repository.
+If you want to run them locally, refer to <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks">these instructions</a>.</p>
+<p class="rubric">Classification Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+</ul>
+</div>
+<p class="rubric">Regression Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+</ul>
+</div>
+<p class="rubric">Clustering Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+</ul>
+</div>
+<p class="rubric">Dimensionality Reduction Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a></li>
+</ul>
+</div>
+<p class="rubric">Jupyter Notebook Samples</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p>There are also <a class="reference internal" href="kaggle.html#kaggle"><span class="std std-ref">Kaggle kernels</span></a> that use Intel® Extension for Scikit-learn* for a variety of machine learning scenarios.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Explore the complete list of oneAPI code samples in the <a class="reference external" href="https://oneapi-src.github.io/oneAPI-samples/">oneAPI Samples Catalog</a>. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.</p>
+</div>
+</section>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/ElasticNet.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-ElasticNet-for-Airlines-DepDelay-dataset">
+<h1>Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-ElasticNet-for-Airlines-DepDelay-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Airlines_DepDelay_10M&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Let’s encode categorical features with LabelEncoder</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;UniqueCarrier&quot;</span><span class="p">,</span> <span class="s2">&quot;Origin&quot;</span><span class="p">,</span> <span class="s2">&quot;Dest&quot;</span><span class="p">]:</span>
+    <span class="n">le</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+    <span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((9000000, 9), (1000000, 9), (9000000,), (1000000,))
+</pre></div></div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_train</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">ElasticNet</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="mf">0.3</span><span class="p">,</span>
+    <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s2">&quot;l1_ratio&quot;</span><span class="p">:</span> <span class="mf">0.7</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">ElasticNet</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.28 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 1.0109113399224974&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class ElasticNet</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">ElasticNet</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">ElasticNet</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 3.96 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the ElasticNet algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 1.0109113399545733&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
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+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="svc_adult.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn SVC for Adult dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/2025.0/samples/ElasticNet.ipynb b/2025.0/samples/ElasticNet.ipynb
new file mode 100644
index 0000000000..29e5dca851
--- /dev/null
+++ b/2025.0/samples/ElasticNet.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/daal4py_data_science.html b/2025.0/samples/daal4py_data_science.html
new file mode 100644
index 0000000000..2c079bdcde
--- /dev/null
+++ b/2025.0/samples/daal4py_data_science.html
@@ -0,0 +1,921 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Utilizing daal4py in Data Science Workflows &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+            Intel(R) Extension for Scikit-learn*
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+      <li class="breadcrumb-item active">Utilizing daal4py in Data Science Workflows</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/daal4py_data_science.ipynb.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="Utilizing-daal4py-in-Data-Science-Workflows">
+<h1>Utilizing daal4py in Data Science Workflows<a class="headerlink" href="#Utilizing-daal4py-in-Data-Science-Workflows" title="Link to this heading"></a></h1>
+<p>The notebook below has been made to demonstrate daal4py in a data science context. It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user’s Power output in the absence of a power meter.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">glob</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+
+<span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="n">sys</span><span class="o">.</span><span class="n">version</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;3.8.10 (default, May 19 2021, 18:05:58) \n[GCC 7.3.0]&#39;
+</pre></div></div>
+</div>
+<p>This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage. Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">workout_data_dd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/cycling_dataset.csv&quot;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>altitude</th>
+      <th>cadence</th>
+      <th>distance</th>
+      <th>hr</th>
+      <th>latitude</th>
+      <th>longitude</th>
+      <th>power</th>
+      <th>speed</th>
+      <th>time</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>185.800003</td>
+      <td>51</td>
+      <td>3.46</td>
+      <td>81</td>
+      <td>30.313309</td>
+      <td>-97.732711</td>
+      <td>45</td>
+      <td>3.459</td>
+      <td>2016-10-20T22:01:26.000Z</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>185.800003</td>
+      <td>68</td>
+      <td>7.17</td>
+      <td>82</td>
+      <td>30.313277</td>
+      <td>-97.732715</td>
+      <td>0</td>
+      <td>3.710</td>
+      <td>2016-10-20T22:01:27.000Z</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>186.399994</td>
+      <td>38</td>
+      <td>11.04</td>
+      <td>82</td>
+      <td>30.313243</td>
+      <td>-97.732717</td>
+      <td>42</td>
+      <td>3.874</td>
+      <td>2016-10-20T22:01:28.000Z</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>186.800003</td>
+      <td>38</td>
+      <td>15.18</td>
+      <td>83</td>
+      <td>30.313212</td>
+      <td>-97.732720</td>
+      <td>5</td>
+      <td>4.135</td>
+      <td>2016-10-20T22:01:29.000Z</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>186.600006</td>
+      <td>38</td>
+      <td>19.43</td>
+      <td>83</td>
+      <td>30.313172</td>
+      <td>-97.732723</td>
+      <td>1</td>
+      <td>4.250</td>
+      <td>2016-10-20T22:01:30.000Z</td>
+    </tr>
+  </tbody>
+</table>
+</div></div>
+</div>
+<div class="line-block">
+<div class="line">The data above has several key features that would be of great use here.</div>
+<div class="line">- Altitude can affect performance, so it might be a useful feature.</div>
+<div class="line">- Cadence is the revolutions per minute of the crank, and may have possible influence.</div>
+<div class="line">- Heart Rate is a measure of the body’s workout strain, and would have a high possibly of influence. - Distance may have a loose correlation as it is highly route dependent, but might be possible. - Speed has possible correlations as it ties directly into power.</div>
+</div>
+<section id="Explore-and-visualize-some-of-the-data">
+<h2>Explore and visualize some of the data<a class="headerlink" href="#Explore-and-visualize-some-of-the-data" title="Link to this heading"></a></h2>
+<p>In general, we are trying to predict on the ‘power’ in Watts to see if we can generate a model that can predict one’s power output without the usage of a cycling power meter. Below are some basic scatterplots as we explore the data. Scatterplots are great for looking for patterns and correlation in the data itself. Below, we can see that cadence and speed are positively correlated.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;cadence&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;hr&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;cadence&quot;</span><span class="p">,</span> <span class="s2">&quot;speed&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;speed&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;altitude&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;distance&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_0.png" src="../_images/samples_daal4py_data_science_7_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_1.png" src="../_images/samples_daal4py_data_science_7_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_2.png" src="../_images/samples_daal4py_data_science_7_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_3.png" src="../_images/samples_daal4py_data_science_7_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_4.png" src="../_images/samples_daal4py_data_science_7_4.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_5.png" src="../_images/samples_daal4py_data_science_7_5.png" />
+</div>
+</div>
+</section>
+<section id="Using-daal4py-for-Machine-Learning-tasks">
+<h2>Using daal4py for Machine Learning tasks<a class="headerlink" href="#Using-daal4py-for-Machine-Learning-tasks" title="Link to this heading"></a></h2>
+<p>In the sections below, we will be using daal4py directly. After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py’s training and prediction classes to generate a workable model.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">daal4py</span> <span class="k">as</span> <span class="nn">d4p</span>
+</pre></div>
+</div>
+</div>
+<p>It is now the time to split the dataset into train and test sets. This is demonstrated below.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">workout_data_dd</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">train_set</span> <span class="o">=</span> <span class="n">workout_data_dd</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3000</span><span class="p">]</span>
+<span class="n">test_set</span> <span class="o">=</span> <span class="n">workout_data_dd</span><span class="p">[</span><span class="mi">3000</span><span class="p">:]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">train_set</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_set</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(3902, 9)
+(3000, 9) (902, 9)
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Reduce the dataset, create X.  We drop the target, and other non-essential features.</span>
+<span class="n">reduced_dataset</span> <span class="o">=</span> <span class="n">train_set</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="c1"># Get the target, create Y</span>
+<span class="n">target</span> <span class="o">=</span> <span class="n">train_set</span><span class="o">.</span><span class="n">power</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="c1"># This is essentially doing np.array(dataset.power.values, ndmin=2).T</span>
+<span class="c1"># as it needs to force a 2 dimensional array as we only have 1 target</span>
+</pre></div>
+</div>
+</div>
+<p>X is 5 features by 3k rows, Y is 3k rows by 1 column</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">reduced_dataset</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(3000, 5) (3000, 1)
+</pre></div></div>
+</div>
+</section>
+<section id="Training-the-model">
+<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Link to this heading"></a></h2>
+<p>Create the Linear Regression Model, and train the model with the data. We utilize daal4py’s linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d4p_lm</span> <span class="o">=</span> <span class="n">d4p</span><span class="o">.</span><span class="n">linear_regression_training</span><span class="p">(</span><span class="n">interceptFlag</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">lm_trained</span> <span class="o">=</span> <span class="n">d4p_lm</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">reduced_dataset</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model has this number of features: &quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">NumberOfFeatures</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Model has this number of features:  5
+</pre></div></div>
+</div>
+</section>
+<section id="Prediction-(inference)-with-the-trained-model">
+<h2>Prediction (inference) with the trained model<a class="headerlink" href="#Prediction-(inference)-with-the-trained-model" title="Link to this heading"></a></h2>
+<p>Now that the model is trained, we can test it with the test part of the dataset. We drop the same features to match that of the trained model, and put it into daal4py’s linear_regression_prediction class.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">subset</span> <span class="o">=</span> <span class="n">test_set</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Now we can create the Prediction object and use the reduced dataset for prediction. The class’s arguments use the independent data and the trained model from above as the parameters.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lm_predictor_component</span> <span class="o">=</span> <span class="n">d4p</span><span class="o">.</span><span class="n">linear_regression_prediction</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">lm_predictor_component</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">subset</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">prediction</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">300</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">test_set</span><span class="o">.</span><span class="n">power</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">300</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_24_0.png" src="../_images/samples_daal4py_data_science_24_0.png" />
+</div>
+</div>
+<p>The graph above shows the Orange (predicted) result over the Blue (original data). This data is notoriously sparse in features leading to a difficult to predict target!</p>
+</section>
+<section id="Model-properties">
+<h2>Model properties<a class="headerlink" href="#Model-properties" title="Link to this heading"></a></h2>
+<p>Another aspect of the model is the trained model’s properties, which are explored below.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Betas:&quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">Beta</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of betas:&quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">NumberOfBetas</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of Features:&quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">NumberOfFeatures</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03
+   8.53155955e-01 -1.09595022e+01]]
+Number of betas: 6
+Number of Features: 5
+</pre></div></div>
+</div>
+</section>
+<section id="Additional-metrics">
+<h2>Additional metrics<a class="headerlink" href="#Additional-metrics" title="Link to this heading"></a></h2>
+<p>We can generate metrics on the independent data with daal4py’s low_order_moments() class.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metrics_processor</span> <span class="o">=</span> <span class="n">d4p</span><span class="o">.</span><span class="n">low_order_moments</span><span class="p">()</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">metrics_processor</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">reduced_dataset</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+<span class="n">data</span><span class="o">.</span><span class="n">standardDeviation</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,
+        1.81623064e+00]])
+</pre></div></div>
+</div>
+</section>
+<section id="Migrating-the-trained-model-for-inference-on-external-systems">
+<h2>Migrating the trained model for inference on external systems<a class="headerlink" href="#Migrating-the-trained-model-for-inference-on-external-systems" title="Link to this heading"></a></h2>
+<p>Occasionally one may need to migrate the trained model to another system for inference only–this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;trained_model2.pickle&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">model_pi</span><span class="p">:</span>
+    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">model_pi</span><span class="p">)</span>
+    <span class="n">model_pi</span><span class="o">.</span><span class="n">close</span>
+</pre></div>
+</div>
+</div>
+<p>The trained model file above can be moved to an inference-only or embedded system. This is useful if the training is extreamly heavy or computed-limited.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;trained_model2.pickle&quot;</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">model_import</span><span class="p">:</span>
+    <span class="n">lm_import</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">model_import</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>The imported model from file is now usable again. We can check the betas from the model to ensure that the trained model is present.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lm_import</span><span class="o">.</span><span class="n">Beta</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,
+         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])
+</pre></div></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
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+  <div class="version-switcher__container dropdown">
+    <script>
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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diff --git a/2025.0/samples/daal4py_data_science.ipynb b/2025.0/samples/daal4py_data_science.ipynb
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2025.0/samples/daal4py_data_science.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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uPn8il0w9grb2UMFJ1YtKZd4USmEtxlhUYnJ2My6afaR40RVG4X9cDhtjzGWdHVxnUKPQNbhNvhDXGLrjwkkltdusDdgZlceFrjnplmfIShLCZ8H3Zk5k1CF9+dpvV7A3sn8b0T/o46HLp2RUFncX5RipeA3GLsAwYcTAikzON/zpdR5clumGckv/VZQk+YxCUYFmY8yXunZISm/CKzgbihbXxCa5egbYF3HwW4JlUdCg3P/C2zkGAeL1Ct/902p8lhB1Mk/oSd94MQHc9BV78/u7Ki6N3bJ5d4ZBgHj67+ypY7rUHaUcPBRlFERkLHAvMMwYM1FEjgdmGmO+X9HRKd1Cvv7EhbJW0tMfk0QcAw58c36Tp0FpfKeNX/7t7bzjyjYIQV9uBlF3UiiAm71iT8p1uBXGdZVrJ19qqxoFpRyKrVO4H/gOEAEwxqwCPl+pQSndS76is3wr87b2EEvWbsFnievzUQceW7kpJz/+hj+9zoW/XJZTvFWIH180OWel3dYe4vl1W3l+3ZZO5+G7FZmlk69uwW3FHnPci+e6srCskqmtysFJsXUKfY0xL4tkfPmjXicr1UcyNfPh5eu5e8lbBGw7r8hb0mVki7AnnKuwmuSHT63lh39ey+0XHM/4wwfwl+YPcibPYhlQ489YYS9t2ZYRpPbbwo8vKi4G4vV+Crl6vFJYiyk6izgOtQG7S5vrFJv+qyjFUqxR2CYiRxOvxEdELgTer9iolC7DzU3hJk+dfHz1GccULF5zcxl5sS8xY3/j900FdwYCnuf4LFj7/i4u/80r+G2LqOMQc0yGXlIkZrhmQekTrJsG0rcXxMX33CZXt7qFYlbmsxrq2BOOdXlh2c3nH8fsqWMqktqqHHwUaxSuBO4DxonIRuAd4JKKjUrpEtxWvwYyjs06sY75K1pdlUS9cAu41gZsPjN5JI+s2ICIpJRNkxQyCLbAX/7jVDbt7ACEDR/u5ebHm7HFImYczp88klufWgvg2vshdR9LSp5gmzftwiLTBRaOOpzz8xf4UZE7D7cVuyWQ7kGa39jK7KljKlJYVj+svxoDpUsoNvvobeBMEakFLGPM7kLXKD2L2+r3mgWrAEMouj/4mXTluLkyvAqr3AKuMWP45qfG8s1PjaV5006+8mAjoWjxUQPLEgbXBjJe56yJw1NprufctbSo+8QcU9IEm+zhkG3EIN5PohTXzs3nH8fIQX350V/fxLYkZyfltyz2hGOd6sOgKJWm2OyjfwDLgBcS/5orOSil87it5m1LwAjgvdJOujLufGadZ+57IYmMU8cexg3nTeC7f1xd9Hj7+OycFX7y5yVrt+C3hGz9UDuxuE+mtfpt4Y4L80+w2RXccx51NwhJSnHttLWH+Okz64jEDBGXXNvkjmDSqEFl92FQlEpTrPtoPDAF+Dhwh4gcC6wyxny2YiNTOoXrat4xFHLkRByHSDRWMPe9kGbQqMF96eOzUjGFQiSDsCs37Ejdr1Aw+4pTj+KCE+rYtHMfxRSHZbvTrjyt3rU+I3tcxe48vOo9+vptHAzXzxifkuUupKekKD1FsUYhRjwdNQY4wJbEP6WX4rWaBzKOzWqo4w+vtKZy6m+/4Hjebdvres9ic9/zuWQAfBLftYRihqDPwgAzJ41gxt1LU+O6fsZ45j6+JscF0zdgE446GGN4aNl6/ufv73L9jPFMHDEw75jc3Gl3L3kLsmIJfluwhILZV264GeKgz+IXXziRDR/uZe7jpWk4KUpPUKxR2AW8DvwEuN8Y01a5ISldhVc/4vu+cCIgTBgxgKUt2/jDKxsSbqX4LqKY3Pd5y97je48147ctYsakJrnk5JvPJfP/ZnyEj44+hOVvt/Gjv76Jz5JUv+LkhP29x9bgz6p/6BuwuGTKKH7z93cJO7A70bnsu39cTW3AzhhHNm6reJ9t8bVTj+ae51pyhOzKce14GeIJIwZwxW8buywNVVEqSbFG4V+AU4B/By4Xkb8Dzxtjnq3YyJQuIV8/4uvPHc/cJ9YkAsJx98y1j67ixTnT8+a+z1v2Ht/9UzxekMwESk5yXi6UdE6pP5TBtQE+98w6wjGT0yEN4iv2fVkuo71hh4eWrcetLCLpXvKabGsDNqGs3g17QjEOqQ3w4pzpOUbArZFOMYbCzRCvLLKFqKL0BorNPloILBSRccDZwH8A1wIq0F4luLlPvvdYMwGfeztKr9z3ls27uemx3DwDWyQ1EXpJZsB+4+I2UaYTiTogAlmCjYXadrpNtqk+0C7ij3OfWMNZE4dTN7gmw9+fTqaCbIyrTj8mr4Jsdryg3P4GqnKq9ARFyVyIyKMi0gLcCdQCs4HBRV5ri8hrIvJ44vGRIrJcRFpE5A8iEkgcDyYetySeH1PWO1Jcce1HbFs5q/T0yap+WH8ubBiVMggLmzZyzs9fcM+siTmpySsumZErfRH0WXzjjLGAt95SbdDGb8fjG9kyEcWQPdmmG8OQ247Espi3fH3e/srprS1DUcOPn17Hx35YvDzFkH5BZp1Yl3FsVkNd3on+vr/9gyk/eJaL739Jeywr3Uqx2kc/AI41xnzaGPN9Y8zfjDH7irz2G8AbaY9vA35qjKkHtgNfThz/MrA9cfynifOULsKrtuDG88YX1ZM51fvYTdYUuPG8CanrZk4eyf2zG+gbsDPOCdhWxmo8W0fols9M5N5LTsASXNVTC+EmmFeoD3Q45nDPkrcy+hlfs2BlSv/I6/pQtPi+x23tIeavaM04Nr+x1fPaaxes5Nan1hKNGfaEHe2xrHQrxcYUVgJXisipicd/A35hjInku0hE6oBzgVuAb0pcPGk6cHHilN8ANxFXYD0/8TPAAuBuERFTTMMHpSBeQdCZk0dy1oThBd0UXrECvy3cNHMCl0w5IuP4hBEDcYz3LgT2+9/T+w20bu8gYNuEopnSWvEaC0PQb7E3nLvDOGnMYG797HE52VGeO5JEYPrK0+q57/m3M14vFDU8vHw9V59xTKcUZJOU0jO5ZfPuVNA9HUvQGITSLRRrFO4F/MB/Jx5/IXHs8gLX/Yx47CH5TR0C7DDGJL+BrcQ7upH4fwOAMSYqIjsT529Lv6GIXAFcATB69Ogih6+Ad21BMTnzrr2PbeHJr3/cUx+omMrdpS3bMoPfM8bnvI4FCHGDEI4Z/BZkhxZefnc75971AndcOCnnPWaPI5nCmjRQ8dTUTO5e0sLFU+KfrytPq+euxetyAtzF1jCUElPwEtYLx0qr1FaUcinWKPyTMWZS2uPFIrIy3wUiMgPYYoxZISKnlTm+HIwx9xHXYaKhoUF3ESVSbtGU1ySfr24h3QglO7K1tYcyhPmyg99zH1/D9eeO5+bH18RrGSIxYgYcB6IuO4R0QlHDf/y+CduCPn5fxm4oX5rpVacfw4+fXpdxLGDHYw3/nUhXFbE4d+JhPLN2CwG7NHmKYg0keKcDX/vpY3WXoHQLRRevicjRxph/AIjIUeTTSogzDZgpIucAfYABxAPVg0TEl9gt1AHJCNpGYBTQKiI+YCCg9RC9iP3unp2AMGJgn4wKZDeG9Avm7AaSE7WXW2XD9r04xkFi4hpbcNspJHGIG5BIooYhPUXVK1YyadQggj4ro7YiHItxz5IWQtH9BuvZN7fwxNWnFN1uNJ1ChimJm7DerIY6rjj16Iwxa1aSUimKNQrXAEtEJNkqawyQt0WnMeY7xBvzkNgpfNsYc4mIPAJcCPwe+CKwMHHJosTjlxLPL9Z4Qu9jacs2rl2wMj7xOoY+/ngQ1qtozG03kJyo3dwqe8NRfpHoyBbxkOSI5d8wZJDP75+eahpzHPy20MdnpyQw4rGGTIO1Jxxz7Q9dzERdzC6trT3EBSeMYubxI3i3bW+OGGGxfR8UpVyKNQovAr8EzgB2AH8hPnmXwxzg9yLyfeA14FeJ478CfptIff0Q7ezWLZSy6mxrD2U0tQFSMhReRWP5gqyTRg3KcKuEY7H4hF9gLWBZ8d1AMYRjMVdfvJuxCvrgB/98HANq/IwY2Id7nmvJuMYrDtDZiTr5N1i9cSdzn1jj6Z7LZ2B1x6B0FcUahQeJS13MTTy+GPgtcFExFxtjngOeS/z8NnCSyzn7ir2f0jWUOpk1b9qFl3qF14q8UJA13a2ysyPMv897lfaQt2dy4oj+vNfWkZK4KMRVpx/jOmF6ZVN9c34TwcRuYVZDHfMbW/PGATo7USf/Bj5LUu/b6z6lZDEpSrkUaxQmGmPGpz1eIiJrKjEgpXsobzLzXsF7raKLCbIm3Spt7SEiBXxDqzftzuklHfRZfHrCMBatfD/neDKDKBs3Y5Xs/xCOxQ3O/MZWHr8qfwyhMxN1oQ522fcptzJaUUqh2OK1V0VkavKBiEwBGiszJKWrcWtI71rhbO0vLnNjwoiB+G33SuV8mTgzJ4/kxTnTeejyKbw4Z7rnbmRIvyCXTTuy4Pu54ISRqaK3oM/iqtPrufG8CfzX2ePwWUIfv0XQZ3FDQqrarehrSL8g15873uXu+0mPIXi9t85M1IUK67Lv41bwpw16lK6m2J3CicDfRSSZEjEaeFNEXgeMMeb4ioxO6TReLqJyJrMh/YL8+KJJXLNgFbYlRGMOV0/PrwOUfi3gqS9UCpdNO5JvfepY5i1fzz1L3uK+59/mnudamNVQh22BhRCKxbjpseZU4NhN/XTiyIH0C9qe7qpCv49kLCAp811qJ7VChXVu9yk2i0lRyqVYo3BWRUehVIRCLqJy2kKWOinlC6Jm7xhaNu/mV0vfzXu/pKBeW3uI/36uhVDUpKqR96dxxif5WMwQSbiCvvXIypw+CdPqhxJ10VdKTsrXnzve04i5Kc5OHDmwpIm6UGFdZ7KYFKVcpJqzPhsaGkxjo3qxvFi5YQeXPrA8IyjbP+jjocunpNIqK5nz7hZETdLHb/HinOkZst7XPLLSVVvpXz92BBNHDMxIz3R7b6WQfP0XW7a5TsqrN+5MFdDFHMMdF+43Ym3tIabdtjgjFpD9fkpB6w6U7kZEVhhjGtyeK3anoFQhxbiIKrXqLCWImk9sL+izuHp6ZgZRW3uInR0RwqUULHi8vlcjogt/8fcMNdhvPbIytcPq6iwgXfkrvYliA81KFdLdgcn0gHYpQVSvcwM+izsuzBzvwqaNTLttMVfOe3V/wVmigM7r1YI+i6yEpbzxguZNO3PkwSMxk6jkrt4sILeEA0XJRncKBzheK+F8j8vBzcdebBDVS2zv4S+fhN9np/SS3GIkfsukJnC3fUNfv80vvnAiO/aGXeMnboH4QX0DHu8ynnmV7I/w4LJMKYrevNrXSmilWNQoHATka8k568Q65q9o7dRk4Sps98SanKwcryCq2yR70pGHcOmvX84Y1xFDanPcNgUaseFgmDBiAEP6BV2No1sg/vGrTsFnkVGo57NgwogBqffr1h/hG2eM7ZWGQSuhlVJQo3AQ4TY5JCfizkwWXj72iSMGuvY/dhtX9iS7tKUtZ1yPX3VK3laf6dT4LQxk7Eiyffde494TjvGTWZO5ZsFKbLGIGYc7LpxUdGVxbwscayW0UgpqFA4ivKQd0ilnssjnYy8miFrsuPaEY6kUTgthb8RbDqMj4jCrYWTeXU++cU8aNYjagM1f12zmU+OHccb44UVdl9nP2eGq0+sL1nFU2ohUawxE6Rk00HyAkx5czNdFLEk5k0W5Ae3k2GoDdtHjSlZH/+ILJ+bIXWQzv3EjLZt3lzXuSx9YxpcfXMEfGlv58oMr+MIDywpeB2T1c3YS/Zyf9eyxnAycu/WHzqbcQLFWQiuloHUKBzBuwUWAaxasTOn8pBNMZPuUG4AsZcWbE9tIE58LxxzOHHcYf13zAZZYGBx+dNHknHEtatrItY+uAvBMfb3pvPH8ax7pjLb2UKo/RDL20PhOGxf+clnOuQu+OpWGI4d4vt98tRNudQyl1Dt0RaC4t7m1lJ4jX52C7hQOUNLjB8mG9Ml4wf2zG+gbsDPO7+u3uX92Q6cyUob0C+bVCco3tqT43FdOPQow/Ln5AyIOhGIO4Rg0vvdhzn2Su4Y/XHEyv5p9outrff+JNwqu0q+c9xpX/LaRF1vinV+ff2ub6/mPrGjNWKVnv99i+jmnU6z+lNffspwdQzF/H+XgRo3CAUq+CWfCiIE4WTvEZJZOT45t086OlHRFdh3bgy+td3UFJSe6M8YPZ1ZDXc7zUcdwzYLcCTTfRHvqMUNdx/3Yyk15XTxJN42bW8vNLVesr78c8UJFKRc1CgcohYK/6T7moM/iytPqO/2axfq8vcYGkrfgrSlxb7fXaGsPcdKYQwi4XG6M4aV/ZHZ2dZtobRFat3fQcOQQPl4/hGz2RpyCq/SZk0fy9+um861PjiXok7w+/GJ9/RooVroTjSkcwCR97l5+6Lb2UEppNF0oLl3jp9wYQSGft9vYptUPzfGxp/NfZ4/jJ8+sy3mNfBpL6cw+eTQ3n39c6r25vdYtn53IJVOOAKDxnTYeWdHKYys3sTftvGz9KC+Sv7/agJ23J0Mxv+dCf0tFKYV8MQU1Cgc4+SacfIHOpS3bip7kyxGIa9m8m6Ut2xjaL8jJRw9JnZec/CJRJ8OFNKuhjkUrN+W8xuNXncKMu5d6GpJsnvnPU1OievOWvcd3/7Q64/nscbe1h/jYDxdn9GouRfyuKyuJNVCsdBUqiHeA0tlm8V5FTc2bdpZUAVtqcdQNf3o9RyJixvEjAMO0+qGpgrdINJZqXr8nHOOp1z/IeA1bhKYNO3JeO+izCEcd1z5xTRt2pKS3gz4rtYr3GvfSlm3E0lw3fluKTufs6kpiFc5TugM1ClVKV6xAC/n2C03yLZt307RhB2OG9C3a5934TluGQYC4RMT8xnhFs98WfnzRpNR7SaaAtrWHcl5jTzjGh3vDLm01vXcNk0cNYmHTRq5dsApL4kVuXuNOTurptxNgUN9ASo8pH1pJrFQjGmiuQspNUWzZvJsFjRtSWTxegc4JIwYUnORv+NPrnPnT5/n2glVc+Mtl/NMRgz0Dpsng8Lxl7/EvDyzPO8ZIzD1byKt95k+eXsf1M8bTx29RG7Rznk9n9smjGVwb4NuPrCQUdTIMQt+AlTHutvYQS9ZuwWdlth8NxwxffbCRk3/wLPOWvZfzGumB8NqATShWfoBYVU2VnkB3ClVIsSvQdPfSnc+sy1ihJ4OuXp3U8nVla9m8O2e1/0JLGwu+OhW/z065fVo276b5/V3MeXQVtkiGmyYftiU0b9rJwJpAxpjc2memaywtWbuFGxauzggKpzN76hhXWWyAcNTwvfMnZASuvcbckdg6fPdPq9kTijLlqCHUDa7JiMN0RKKISGrVlZT3Ltb1pKqmSk+hRqEKKSZFMX1SCUWjZM9tD760ntlTx1A/rL+rrzpf282mDTtcx/Vu215Wte7IMBgCrr79fERiDl95sDEnI6pucE1O+8z0NNvTxx1G+P+8XUe/f2UDQZ+4Phd1DHMfX8OUMYe4Ngfq47dcg9m3PrU2LtMRc4jGDA6kGev9Y3Ucw5Nf/3gqyJ2PfLEIQIPNSkVRo1CFFOqv7DapuJEMuuZ7HbeJZ7JHKubgvv6cHUSpBsFvC8YYQjFSvZdL6Stt8pihB5a+k/+1LcsjcC1E8nR5K2YHFPTZRe+UvHaC85av57+fa9Hdg1JR1ChUKflW8sWojoL35F6I5e/kSk4A/PE1bzG3dII+KycY7LeE7547jqMO7c+V817N0A9Kd42NP3wA1501LieVFeId02JO+SnW4ZjD5FGDXALXnU/bLiWW4LYTDMcc7lnyFqGo0Z4ISkXRQHMV46Vl4zapZDtNZp88uihXRpJk0LNl825uWNTses5Tqz/wvL42aOOz4s1qLBcPjgicUn9o3iB3Mrh902NruOp3r3Hns+syzvt7VtVyqUSiDsvf+TAj+B7wWQRtd5dTKVw/Y3zRk7dbAsBVp9cTsDMD6Sp1oVQCLV6rYvLVKbhVwI4/fABNG3YwedQgV4PgVVCWHp/YE4oW2H+4c82nxvLzxS05O4SABWEnMxALuI79zJ8+n3PfZDFaW3uIqbc+U7ATWzHc8tmJnDVheKoa+dy7luZNcy2E34IF/zatYAV0Nul/X6DkAkFF8UKL1w5ACmWneLmXvHYH2QVltgg3nz+BUYfUcM0jTYRjFHRHFSJgZ7qN+gZswjEHMKnJ7tpHV/HinOk5HdsWNG5wveeilZv44sfG0Lq9A0ss3Do12wKOKT6+8b1FazhrwvDUJH7ZtDHc+7e3S3inmUSceG/qYsg29OkTfqF4iqJ0BWoUqpBiK2WLrYB1SzGNGcN3/7QavyVdsvq+89m3cvz9UccQtC2isdyK4my3WNuesOt971nSwi+f/wc3zJiA8TBaf7hiKkce2o/mTbvY1RGhedNOHlj6jmtqKsT99w8vX8/VZxwDwMlHD/E0CknPksetgLjLbGnLNgbXBvL+PQp1bcsXR1KUrkJjClVIV0spe6WYAkQ6EbhNJxzLlMMW4IsnH5GT1bMvEiUSjfH8uq08v24Lbe0h2tpD/PSZzPhBkpiJB4JvXLSaG2dOzHl+ypGDaTgy7go7deyhnHz0EP7n7+96GoQkdy1+K1U0NmJgDbbLN8WW+OsXuBVRB256bA0n3fIMdz37lmsxWnZBolfXNu2JoFQa3SlUIZ2RUnaLQ5SbhdQZDHD/C+/kBMAjDhldz/y28PXpxxTMpoo6MLCPPyezafk727nvb//gik8cDbhnZlkJ91I64ZjhgaVvM274AK5dsApbLGI4+G1BgItPGs1DLhXNABedMJJHXs3NxIoZ+PHT6/j5s+u4aeZELpl6ROq55k07sSQ3oB2KGq5ZsJJBff1MGDEwVW3dvGkXYFLHoDgtLBXVUwqhRqEKKSZfP0n6JLC0ZRvXLliJLRZRJ8bV08dy8ZS49MOZHzmUZ97Y2u3vpdA+JBIz3L2kpYgzYVv7PnyWkL0Ov/WptdQGfVwy9QhXg+q1Gbr3ubdzqh6MgX8/7Wju+ds/8MpUDfpt+vgs9nkEpyNOvBoagUumHJHQYnJvkQpxw/C1h17FMYZZJ9bx8MvrU3pMSa0oAwUroLVKWikGzT6qYgqt+rJ91G7KobaAZQl9fDYdkRgxx+BPZAT1Fmr8Fv/80ToWvBoXzfPKBFrw1alc/MBywi7+HJ8t/DlRUbyoaSPXLFjVqYyifBQTZwAI+CyevLo06W/X+9iCiOSV9y5H3lw5cNEezQco6f7lbLE7Nx+12xwVM/HV+O5QlKhjCPgsZv3TaNfXs4inVxZL57P743REHP7UtBEwnDbWvVWmBfh9Nt/+9LGuz0djhnN+/gKLmjYyc/JITnHprNZVxIz37iMdv71f+jvjuBV/rq+/uIwliOtFZd4jM8akLT2VYlH30QFAdjrp7JNHc8EJo4qqas4mYFscPrCP63PfOWccP/zz2qLv1ZV70KRExDNvbHF93gHmLX+PRSs34bdwzZgKxwzfXrCK2oDNs2sr6yor5r3HHMOYIX0JRTPlL2w73jxo0859XP6bV1x3PumEYyaj5wPkxpi0padSLLpTqHLc+hM8+NJ6ItFYziRQDBHH4dMThues8i2BY4f3J5Cnh3IlyO65nG9+nN/Yyr6IkzeFNhx1uPzBFV0zuE7gs2DmpBFc/KuXkUSAOWhLSr67flh/Th17KGd+5LCi7hczhqDPu9dzsf2gFUV3ClXMwqaNfGt+k+tz77bt5fYLjs/rO0+mVCZJdhVrfn8XPltSaZsCzD1/IhNGDMTdCVU5OhPb6BuId2DLfvu9IYrmOKQaCyUxIjxx1SmpAsO29hBPrd5c9D2/c/Y4Pjp6cKqbXHYjIK1zUIqhYss+ERklIktEZI2INIvINxLHDxGRp0XkrcT/gxPHRUR+LiItIrJKRE6o1NgOBNy6gqUzedQgZk4eyd+vm863PjmWoE/oH/QR9Anf+uRYFnx1KnZW8r0lMP7wAcx5dFVGHr8Bbn58DS+2bOOOCyfRBVJA3ULMMXzl40d1WWyjK3H7s/nt/f0b2tpD/P7l9SUZsFPqh/Ju2x5m3L2USx9YzrTbFmfUOEBmHOpAaOJzILyH3kYldwpR4FvGmFdFpD+wQkSeBv4VeNYY80MRuQ64DpgDnA0ck/g3Bbg38f9BSaHMonxKqOlid0P6Bbl4ymgmjRoICBNGDGBIvyArN+wgaMdX0kkCtu0qHQ3xjJ+kBMVl047k/gIy1D1Nsvbgt8ve6xU7g2LYF45RN7gm1S7UKcH9d/zIAQBF94Q+ENJTD4T30BupmFEwxrwPvJ/4ebeIvAGMBM4HTkuc9hvgOeJG4XzgQRPPkV0mIoNE5PDEfQ4qivmwuwUOfRb8/itTU32N893LK/DoJh2dej7q0LxpZ683CBA3CE7MEIkV18OgdyD8tfkDbljUXLDiOpu3Nu/mnLuWIlkp5l4d+Yo1Hr2VA+E99Fa6JWooImOAjwLLgWFpE/0HwLDEzyOBdNWz1sSx7HtdISKNItK4dWv3F1tVmmL7L7sFDn8ya3KGQch3L6/AY/2w/lx/7nhXl0vMwN9btlX4N3DwEjOG7/xxdckGAaAjaghHHUJZ14aiUWoDdoaL5UBITz0Q3kNvpeKBZhHpBzwK/IcxZpeklfIbY4yIlPQNMMbcB9wH8eK1rhxrb6DY/svgHjhMdzt53at50y4G1viZVj80R4103rL3uGFRs6fL5RfP9/5dwsGMz4KYsz+YHo7Bp+98gb7+/a1Np9UPrfr01LrBNezLSuXdF41V1XvorVTUKIiIn7hBmGeM+b/E4c1Jt5CIHA4kE883AqPSLq9LHDuoKDafPH3yT0o8Z1Ywx7jslCMJZ7lPOiLRRP/jXCXOecvei8svKFWLW+JBzDGpTnbJuJCbTArAyg07qiYzKVuNoZrVGXoTFTMKEt8S/Ap4wxjzk7SnFgFfBH6Y+H9h2vGrROT3xAPMOw/GeEIxukZucYJp9UNzfKz3Pvc2dpbIWsyBqOOk0lR//PQ67l7yFjecN4HvPebeUU2pHvxpqcRuWCI0b9rJzMkjM5ouNb+/i2m3La6aoG3r9g5q/L6Mtq01fp/rjlopjYppH4nIKcALwOvsz8D7L+JxhfnAaOA9YJYx5sOEEbkbOAvYC3zJGJNX2OhA1j7yyj7y0rC57wsncuW81zK+JKXgswUfhn3VFJdVcvAJnkJ9SQK28PmTRjG/sTW1q3QScidJersuUlt7iCm3PpOxM/JZsPy/zuy1Y+5N9EjnNWPMUrzlb85wOd8AV1ZqPNWGV4McrzgBCB2R8gwCxLWByr9a6S1EjbsUeDrhmOHBl+JV8F4yKF5xrN5EfB1psh4rnUVlLqoMr5jDiIF99EuhAMWJ8RWiuwPPpRahtW7voI8vUzCwj8/W7KMuQGUuqoikS+n6GeOZ+/iaDP/vnnCMPj6bSEzX+wr4LCFagnWwAMuKy3k7hm7VRSqnCK1ucE3OzrgjEtXsoy5AjUKVkP3Fuf7c8UwcOTAjFTUcK74CNh8+C4wDGl6oXuaeP4G5j7/B3khxf0WHuB5TNOwwq6Gu24LMLZt3c80jKwnHTMqVdc2CVQzqG0hV33uh7qPKoO6jKsCtCG3uE2sygtBLW7blyCeXS43fx9dOO6pL7qV0P7NPHs2nJgwvW7xwfmMrj6/cWHE9oYVNGznnrqU50uChqMPXfrvCVbsJ4t+HJWu3EMjS7lL3UdegO4UqwC24bIuwZO0WTh8Xl1bOJ45XKnvCUe557u2uuZnSrfxq9olMHj047mY8dzxzn9jvZvzmmWMJ+CxufXJtwV3ltx5ZhQhcP2M8E0cM7PLaheRCJ+zxoU3ucNKlK9raQ8xbvp57lryF37ZS4oFJqq0Ar7eiRqEKcAsu7wnHuOmxZv7fwtVceVp9WQ11/Am50+y89q4IVCrdz6yGkbSHY0y7bTG2QChmuPITRzP9I8NSk/rCpo04Jn1x4d6jIlnH8t0/rqY2YBN1DBecMJJ//uhI9kYcwDBhxMCyDUXr9o6cGho3kllQ8f7i+2Xg0xsT1QZsYsZof4guQo1CFZBe0GbLfnnl9lD8/58vXkc55SaRmMFn4dmpTKkeTj5yMKcecxjfnN+U4Y65c3EL7+/ax+0XTnKVW7cS+av55JaSn7eHX97Awy/vlyfz28KPL5qUEX9o2bw7VRCXVOrNpq09xJ9Xv5+z0g/Y8RhB+uGI41AbsJnzqHtfkNqgzffOm8Dp4w5Tg9BFqFGoEpI6R0vWbuGmx5pTBgGgyFiiKxXqXa90My+9s52X3tnu+tz8xlau+PhR7AnHcnaUAZ9FJOoQK0OELxIzXLNgFeMPH8CecIx5y9/LaBx0znHDmHv+cQCpQszsFX86//aJo9naHmLBio34bCHmmFRmnddOOOYYNQhdjBqFKmJIvyCnjzuM/7ewsD6RD7QYTUkx+9fL+dK0I+nIWkGEow43JSROCvWCdsMxhnPuWopPSLiV9vPk65v5y+rNWJbQx2e7Vk6nc+filtTPMRNPqwV39ylA0CfqMqoAmn1UZSRdSYEC7c/UICjpbNoZ4pYn1+bULhhjOGvicL79qWPLum8kFpfszjYISWIJI7A7FCUUNUXLgsccCEUN1z66CiBD5j3os/jWJ8fy9+vO6NX6TNWK7hS6iUKd1EphWv1QfjJrco7/WFFKJWBbPLZyE7f9eW1PD8WVZKBZ+0t3H2oUuoGubBuYfi/HGPy24LctIjEHS8TVV6soXuyNOPzwqTfyBpqTWAJBGzq6cRuanmbqpQemdC3qPqowxXZSK+deUSe+NXccg2BwOql42z+oH4eDkX2FZFUTOAb2FTAIFjDtqCEEfXFXj8+KZynVBuz8F2bhtyXVDVANQfeiO4UKU0ontWLuZVyKCPYldgc+yxD0WQRsi33RWMltHXeHHPrY8fTU844/nKdWf5DT3lGpfmwBsQS7wM4yYEuOe7LQp8EBVmzYzhNXn8Ke8P5OaK3bO1j+Thu3PpnrprrgoyP4t9PqAWjasIMxQ/ri99nqJuoh1ChUmGI7qRVDJBrLO0nX+H3cc8kJNG3Ywc+eWVfy/YFUP4Wnmj8gpp2sqp74Aj1zco8Z+PxHR/LHpk3e11lw+rGH8Zc1m0t+Tb8VrzZOdgQEUpN7v6CdkU5dG7CZ/bEjUzUNXrUNSveh/oIKk8wWSmZOlLslXti0kYsfWJ73nKSE9j1LWlyrkoM+i1s+O5EHLzuJS6eMznsvn21x+rGHeT4ftAW/TY7+jNK7uOjE0fhd/ka/b2wlGnM8XTthB5a2bC3rNb0WPXWDa3Kyn2LGqDRFL0N3Ct1AZzMnUjoxHruE9DL/PeGYZ2ej75w9jkumHAHAiIF9eGj5es/XDEUcnn1ji+fzMRPvoBTuIhE+peuxBb40bQyPrNjg+nzMxDu13XvpCWzY3sHcx9dkVMzvCXv/bS1ALAgmen2LxGsR3NrHJimm1azS86hR6CY6kznhpRMT8FnceF6mYFlbe8jT7ztxxABWbthBbcBmTzjGrIaRzG/MVaGEeKVoPudRKVr9SmUJ+izX2IBlCYNrA9x43gS++yf3gseAbTOwJsCpYw/jrAnDXSvmawM2508ewaOvbiRgZ/YFTy50gKIWPZpa2vtRo1AFrN64M0cnxmcLD3/5JBqOHJJxfEi/IHdceDz/OX8lsbSJ+5T6IVz665cB2BdxCNoCIq6CaJ/8yGE8nWeXoHQvtsBl047k/qXvuD4fdRz8dq7cScAXT2i4ZOoRIHDTouac5IPslE+3ivmoYzhr4uFcNu3IVPA4OZmnT+rFTvCaWtq7EVPFwcSGhgbT2NjY08OoKG3tIabdtph9WRWjAQss2/KseWhrD/HSP9rY1h5i4ogBXPrrl3Pu4UU+gbygL+6fjjmO6iZ1E7+afSJ+n8XsX79S8rW3fGZi3CgQ/0w8vHw9dy9pyVjxZ39+FjVtTLl49kVjGGOo8fs6XWOj9B5EZIUxpsH1OTUKvZuVG3Zw6QPL2R1yTxDv47d4cc70vCuvQvcoloANt372eE4fdxgvtmxLSRDsizjYaKe2SnDcyP68tWUPlgh7w7m/4UJtN/v4LR6/6pSMFX4x1fVt7SGaN+3kKw82EkqrYyjm86b0fvIZBXUf9XK8xMCSFFPzUOgexWKQlCLltPqh3PeFEwFhxMA+rP1gN1f97rVOv0ZvxLYEyxgiaXNv0BbO/Mgwnlj9Qcn3ijkmpV1lWZJ3B/f6xt1ljTmJceKCdcGsnUGhSX1IvyADawIEbJtQdP9iotwaG6V60HzCXkRbe4iVG3ZkVDunp7TWBnNTB4upeUi/h7+AkF4+bjpvQqpRy7TbFnPlvNe44reNrHl/FzMmjWD2yfnTXKuVi0+qQ7LSOsUSbv7MRL5z9jisEn6lqTiPCA9fPqXTY4s5xjPbDOKNdsLR8qrpu7LGRqke1Cj0EpIT7aUPLM/pTTtz8khenDOdhy+fyi2fmehZ8+BmVNLv8fhVp5Q8roAtBGzhls/GfdP5ZDtuPv84nvnPU/n69PqSZQ3KpTvqJH67bAORRAAl6LMI+CyunzEegJ8+sy6nJsRvC2d+xLvGI3nO6k27+NLHxhD0uRv8YshsXV+Y5Eof8n9eoOtqbJTqQt1H3UQ+P276RJuUw0jvTQv7MzYmjRrEWROH59yrGNG9PeFYQjwv1zedaMDlypNf/3iq0rSQbEf9sP588WNjuO+FruvxbEvczeIm22Fb4DPJ95Ary+CFT8C2JcNfno/kWaGoQ1+/MPfxNXzYHk5p/qcTixmeX5e/8GtPKMZNj62Jj8WCC08YzR9fa/WUoPaiVKdgcqVfrEijppAefOhOoRvItwuA/RNtOukrumySxiF9h1CM6F7d4JqMNNV0vFwQfl9mg/RiXApD+gW5/tzxnm4VO+0JyfPaSe78/EdZ9p0zuOUzEwnYQl///t9VRySeBWVbFg988Z9Y8NWpnHvc8Lz3C/osfvb5jyJF9Ah2Y2/EsC/icNfida7xAAdKkjSPOrDg1VaiJcZ9ihm9zyIlTpdc6QMliTRmf96UAxs1ChWmZfNurlmQ/wtYzESbb6tfjFFJ7lRuOG+8a1zBaw6LRE3OhF/IpbCwaSNzn1hDjT/XJSLEV/5JDPndH+dOHM6MSSMY0i/IWROH88AXG7j2rHH0y3K3BGyLpg07uORXy3ni9cLB3wE1fr75ybEFz8tHOAZOiUV8QZ9FwMVa2pZw0YnFx2ROPWYIP/jsxAKvJfxk1mT+ft10Hrp8Ci/Omc7MySNdhRWNYzwXIcrBhbqPKsjCpo1c88jKnFVjdgZHofJ/r61+cqKvDdh5jUr29TedN4FIzOG2p9bSUaDYIOY4vNiyLcO1kHQpNG/aCQgTRgxIPZc0guGs+/oswMBXPnEUD720nnCscHqsT+BzJ42irT3E0pZtqfeQbOuYTjjmcPfitwqu0C2BaMzh3x5aQSSRBeR2TT53Wjpl5XRJ7o1jjuFL08aw4NXWonpivPzudm6YMSGVzZRN34DNLy49gVPHxmMb6Ua7NmDnCCuGYqZTcaCubCJVja9/IKFGoULk0ytyy+Dw8t16xRt274sy94k1qYl+VkMd8xtbc4xKy+bdKcOUvP57jzXz8OVTcLLW6H5bEAzp6fAxA9csWMWgvgEmjBiQGlf6JJ18vd37otz4WDNRl/ecnOfuf/7tHLeNbcXbL2ZjBK6c91pGb9/ke0i6RZJFWNOPPYwnC6SHpldvZ1eIZxPwWXz140dxz3Mt+K3yG8sI8erzdF2gxnc/5MFlmbpT508+nD3hGDfMGM/cJ9ZgCezNoz2U1Cj66axJfPuRVYSzfoGOMUwYMdD12j3hGH38Vobrq4/fKvg78aIrm0hV4+sfaKhRqBBuAVmIT7zXzxjvKRiWfdztPrYlqUbryePzG1tzipQWNm2Mr9qzJulwzPD5+5dnNOXxWfDjiyYxqK+frz30akahVCjq8NUHGzFCSvMm21D95x+aiureFXXAFpMxoX/zzLHc+lSuzn7MwbPgLikTPrDGT23A5ty7XvB8zYBtIYkdQjH4LPjmJ8fyo7+8SdQho3K7xm8TjsaKeq8QNy7ZvQWuWbAy57z5jRt5YtX7RGKGf/vE0bTtCecVLIzE4guLSaMGMa1+aKJS+S0Cdn5ROsAzpbScVNNikiSKvU85K/2uen1lP2oUKoRXwZjfimeu9A/6ilrNuMYbYga/HXej7L9vpoZ9W3uIa13cOEmyq2Bty2Ja/dD4cy7ZSUk307WPruK+LzTkGKpSevEEfBZzzhrHUYfWMmJgDU0bdpScWrknFE3tXFZu2JFTZAXwsaOG8Mq7bYjE3WA+2yJWhGsm6sBtT63NeU8Bi4TsQ/HjtERY2rKV8yaNTI3VFgu3+u+kKumdi1sKBvtuTNSMQHwxcfUZx3DxlNFFTaxdqVbaFU2kOrPS78omVkocNQoVIv2LZ1vCnoTqZDLlsNjVjNsX+PoZ45n7+JqM8yKOQ23AZuWGHdQNrmHe8vUl9WsO2PEv0qMrNpDPixBXazWdqpDuiDj86K9vEoo6GGPwW1KSQYC4L/+2P6/l9gsnuRpOvwXL3/0w7pZKPFdKdo+bkcvjzQHiWRvZp3REYtz02Bvc8uRaLj/lSCaMGFDUOPKd8V9nj0vpGaWT/CwlA8bdoVba2QK3zq70tcCu61GjUEGSX7wla7dw46LmDJ9tKasZty9w/6Avw1DMOrGOGXcvTQRinaJdJUk6IlEi0ViOrzubSMxhwoiBXH/ueL73WDN+2yLqOCmff7GkSzN7aff4rLibKBSNQVasA+Iusys+fhT1w/pnGM6OSBQHXAOwflsI+CzCUQcLurTd6HVnjyMUdbh7SUuOQY7EDPf+rfO1G+kCd9nkW3G7uWe6Qq20s7uOzq70tUdD16NGocJ4yRHnW80U8wVONxS1AZsZdy/NWG25ce7E4Tz75paMnUsSEWH1pp0F38+N501gacs25j6xJj65xgw3njcBTDyAHY0Z1xHU+C0unTqa3728IcMg5OPK0+uZPm4YtQGbT//seddzlrZso35Y/7SMqF0JETf334Mxhq+dejRnTxzOuXctpTSnVX5+8sw6XpwznUmjBvGV37xStMFx22GkYwt8/zMT+dSE4XkF7LxW3G5JAV0ZiO3MrqMrVvpaYNe1aJ1CNzE9q7XlrIY61w9voUK3JOmGY084llOnkI3fgn86cjAPXXYS3ztvQk76YR+fzdB+fTyvtyTutjhr4vDU5NMeihGOOty4cDU3Px43Ej7b4uyJuRIPkZjD5xpGl9Sc55fPv03d4Brqh/XnCx6r46FZev4Da/x5pS+iDty9pIVNO/dx2bQxrufYgmvhna/At8W2hCVrt7D2/V0l7UBqgz7+6YjBns/7En/b1u0dngVmXrUqzZt2lVSoVi7lFrh1lZSGFth1HbpTqDALmzZy7YKVOXIK8xtb+cYZYzM+xPlWe7C/s9XSlm1cu2BVKkf9hvPGF/TxRxy46bE3AJg56XAisdzV2clHD0mltmbjGLj9L2sJRZ0caYd4ho5DMlHoqdW5DXpE4l3Actw8DgT9FlHH5KTDprsRrj7jGH67bD2xtCivbQknH53ZZKgYRdhQ1OFrv11BzBjXJkPpj5MurI5IlEJKQ3tCMW5ctDpvG0uv8axYv937+ZjDd/64GlviFebpXc+SXfRqA3ZG4gGQ+D2YXh+I7e6VvtY05EeNQoVI6tFf80iTa+DW7Yvp1nbTEuHnz77F717ZQMAWIrG4rEO6v/ymRc1cfsqR/GrpO0XJKyxa+X6iWY7Bb8V1ha6fMZ7mTbuYcfzhBGyLecvX50x/UQd+8vS6shwuyVXrzMkjGTGwD/e/8A7Prt2Cg6EjEvfv23bmpBuOOfy1+X3+2vw+Hzl8IDefP4GbEwF2YwxXTz+G7XvCGV/wjAC/CPsi7umjexNtyiziOwO/Bftc/k6OA18/o57b/7yWiMcuJ+jbr6FUqkFIvs9iiBmIRRy+/vumRE1JPL046LNwjMn4TNgCV55Wz4iBuUYyHHPY2RGhrT3ULZNiepFldue29Ak6mTnndX2htp/FTPa9saahtxmpXtVkR0TOAu4EbOABY8wP851fbpOdGT97juYP9jBheC2P/8dpZY01H8ndQczxDqIGbPivcz7CKfWHAtC0YQdPr/mAv6w5cNtgJuUtOhPbHRCMu0h2hbTtW7HU+oU9Efdf+qhBfRjSL8Cm7fvY0RFGgD4BH0HbYue+MLFY3ExbQN8ai4F9Ahw+IEjL1j2EojEG1AQYObCG2j4++gd91B/Wj1fe3c66Lbs4fuQgJowYyH//7R9EY/Ge3347vrsd1MeHEcP2vTH8VnzBceLogRxSGyDosxkxqIa2PWE27tjLsre3k0zitYgX2kUd+OcTDmfU4FoCPou/rH6fxvU7U/GZYf0DnD95JF/9xNFs3xNmactWIjHD7X95MyMhwraEWSeOZFLdIAzxXdsp9UNTApDpBm3Tzg42bu/gwz1hjhhSy7jh/dm0s4M3P9jNmx/swm9bHFIboG5wX0YOrqGv32b1pl1EYg479obpG/Dx6QlxXa6lLVsZ2q8Pm3Z0cPuf12JZ8QXRjy6anKNWsGlnB0nlADdjWo4hqYrOayJiA+uATwKtwCvAvxhj1nhdU45RGHPdEznH3v3huSXdIx9t7SGm3PpM3laVpebkK4rSvcw+eTQnHnEIcx5dhXFMl2ap5UOIJxXMfWJNzuv6LPjJrMkY6PRup1qMwsnATcaYTycefwfAGPMDr2tKNQozfvYcqz/Yk3N8YhfuGJ5ft5XZv365S+6lKErPEfRZJdX6dBV+210mHuL9TUTodIvUfEahN2UfjQQ2pD1uTRzLQESuEJFGEWncujW/Zn02zS4GId/x8ugdRlZRlM5Rfo/CzmHlkXQXkURF/H7yyeyX9fpddqduwhhznzGmwRjTcOihh5Z07YThtSUdL4cJIwZ2quWloii9g964vDPGEDOVreDuTUZhIzAq7XFd4liX4eUi6spg85B+QX580SSCPou+LlLExw6rPWB7GSvKgcLsk0dzx4Xx+olgNy7y8r2uz4IfXTSJOy6cVNEWqb0ppuAjHmg+g7gxeAW42BjT7HVNb80+gszsgKb12/nrms18avwwzhgfzz5o2bybpg07mJxIw8v+2WfB/774Hht27OGoIX3ZtjfKqEE1HFc3kI8cPpBxw/uz5M0tvPSPbUSjhr3RKCMG1PDh3jA+WxjcN4BlCbV+H607OhhQ40cwbNsdBmBXKMJHDh/A1CMP4X///g5vb93LyEF9QOCdbXuxMAzuF2Tsof3ZvjfMlvYQ23aHCPptJo8aSMBnsXLDTvaEIoiAMWBZYIlFJOoQiUGfgMWAGj+zThjJ9PGHs3jN+zz08nra90ZB4umeDvGspJqgzaH9AuzsiLCjI0rUia/U/IBtg9+28Pss+vhsLIGd+yJEYw4xQ16tpnQEUpkupXqK7cT1DrnXBhLf3aiJ53iLQCjxtfIBAZ/gs2FfNFeqo9L09cGQfn3w+YS+Ppu6wX35YFcIxOA4YDD4LKlY9tHXPnE0b21pZ8V72zlsQJCxh/Xjydc/4I33d+EYw6S6QYwd3p/W7R1EHcPeUCQj+8iXSL2NxAyjBtcwsG+AiSMGsDfisHH7Xj7cEybgs1izaSfrP9zLUUP7sWl7B1v2hDj92MMyso+CPpv+ffyAYUBNgEg0xpI3txKwhbHD+vdI9tG44f1Z+8FutrXv45T6Qz1f96DMPgIQkXOAnxH/Dv7aGHNLvvPLNQqKoigHM/mMQq8qXjPGPAk82dPjUBRFOVjpTTEFRVEUpYdRo6AoiqKkUKOgKIqipFCjoCiKoqToVdlHpSIiW4H3yrx8KLCtC4fT3VTz+Kt57FDd46/msUN1j783jf0IY4xr9W9VG4XOICKNXilZ1UA1j7+axw7VPf5qHjtU9/irZezqPlIURVFSqFFQFEVRUhzMRuG+nh5AJ6nm8Vfz2KG6x1/NY4fqHn9VjP2gjSkoiqIouRzMOwVFURQlCzUKiqIoSoqD0iiIyFki8qaItIjIdT09nmIRkVEiskRE1ohIs4h8o6fHVA4iYovIayLyeE+PpRREZJCILBCRtSLyRqKFbNUgIv+Z+NysFpHfiUifnh5TPkTk1yKyRURWpx07RESeFpG3Ev8P7skxeuEx9jsSn51VIvJHERnUg0P05KAzCiJiA/cAZwPjgX8RkfE9O6qiiQLfMsaMB6YCV1bR2NP5BvBGTw+iDO4E/myMGQdMooreg4iMBL4ONBhjJhKXp/98z46qIP8LnJV17DrgWWPMMcCzice9kf8ld+xPAxONMccT7x3zne4eVDEcdEYBOAloMca8bYwJA78Hzu/hMRWFMeZ9Y8yriZ93E5+UcvpY92ZEpA44F3igp8dSCiIyEDgV+BWAMSZsjNnRo4MqHR9Qk2ho1RfY1MPjyYsx5nngw6zD5wO/Sfz8G+Az3TmmYnEbuzHmr8aYaOLhMuLdJXsdB6NRGAlsSHvcSpVNrAAiMgb4KLC8h4dSKj8DrqX05mc9zZHAVuB/Eq6vB0Sk65p7VxhjzEbgR8B64H1gpzHmrz07qrIYZox5P/HzB8CwnhxMJ7gMeKqnB+HGwWgUqh4R6Qc8CvyHMWZXT4+nWERkBrDFGLOip8dSBj7gBOBeY8xHgT30XtdFDgnf+/nEjdsIoFZELu3ZUXUOE8+nr7qcehH5LnFX8LyeHosbB6NR2AiMSntclzhWFYiIn7hBmGeM+b+eHk+JTANmisi7xN1200XkoZ4dUtG0Aq3GmOTObAFxI1EtnAm8Y4zZaoyJAP8HfKyHx1QOm0XkcIDE/1t6eDwlISL/CswALjG9tEjsYDQKrwDHiMiRIhIgHmxb1MNjKgoREeI+7TeMMT/p6fGUijHmO8aYOmPMGOK/98XGmKpYrRpjPgA2iMixiUNnAGt6cEilsh6YKiJ9E5+jM6iiQHkai4AvJn7+IrCwB8dSEiJyFnHX6UxjzN6eHo8XB51RSAR6rgL+QvxLMd8Y09yzoyqaacAXiK+wmxL/zunpQR1EXA3ME5FVwGTg1p4dTvEkdjgLgFeB14l/93u17IKI/A54CThWRFpF5MvAD4FPishbxHc/P+zJMXrhMfa7gf7A04nv7i96dJAeqMyFoiiKkuKg2ykoiqIo3qhRUBRFUVKoUVAURVFSqFFQFEVRUqhRUBRFUVKoUVCUXoKIPCcivb6xu3Jgo0ZBURRFSaFGQVHyICK1IvKEiKxM9CH4nIi8KyK3i8jrIvKyiNQnzj1URB4VkVcS/6al3ePXiXNfE5HzE8drROT3id4MfwRqevCtKgoQF/lSFMWbs4BNxphzISWhfRtxldHjRGQ2ceXXGcT7LfzUGLNUREYTr5r/CPBd4pIelyUaq7wsIs8AXwX2GmM+IiLHE682VpQeRSuaFSUPIjIW+CvwB+BxY8wLCUG/6caYtxMChR8YY4aIyBYyexQcChwLPAf0Ia6MCXAI8GngB8DPjTGLE6/1KnCFMaax8u9MUdzRnYKi5MEYs05ETgDOAb4vIs8mn0o/LfG/BUw1xuxLv0dCgO4CY8ybWccrNGpFKR+NKShKHkRkBHEXz0PAHeyXy/5c2v8vJX7+K3HRvOS1kxM//gW4OmEcEJGPJo4/D1ycODYROL4y70JRikd3CoqSn+OAO0TEASLAvxFXGx2cUEsNAf+SOPfrwD2J4z7ik/7XgLnE4w6rRMQC3iEeg7iXeCe3N4gr9lZj8yHlAENjCopSIomYQoMxZltPj0VRuhp1HymKoigpdKegKIqipNCdgqIoipJCjYKiKIqSQo2CoiiKkkKNgqIoipJCjYKiKIqS4v8D24TqJllpg8wAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2025.0/samples/dbscan.html b/2025.0/samples/dbscan.html
new file mode 100644
index 0000000000..13a2e8dd4c
--- /dev/null
+++ b/2025.0/samples/dbscan.html
@@ -0,0 +1,701 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=132153fd" />
+      <link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/dbscan.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-DBSCAN-for-spoken-arabic-digit-dataset">
+<h1>Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-DBSCAN-for-spoken-arabic-digit-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">davies_bouldin_score</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;spoken-arabic-digit&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Split the data into train and test sets</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 6.37 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_opt</span> <span class="o">=</span> <span class="n">davies_bouldin_score</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn Davies-Bouldin score: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class DBSCAN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 469.21 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_original</span> <span class="o">=</span> <span class="n">davies_bouldin_score</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn Davies-Bouldin score: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn Davies-Bouldin score: 0.8542652084275848&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare Davies-Bouldin score of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Davies-Bouldin score of patched Scikit-learn: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Davies-Bouldin score of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">score_opt</span><span class="o">/</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="kmeans.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+   
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+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+</html>
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diff --git a/2025.0/samples/dbscan.ipynb b/2025.0/samples/dbscan.ipynb
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2025.0/samples/dbscan.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/kmeans.html b/2025.0/samples/kmeans.html
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
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+</ul>
+
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+        </div>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
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+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/kmeans.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Kmeans-for-spoken-arabic-digit-dataset">
+<h1>Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Kmeans-for-spoken-arabic-digit-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;spoken-arabic-digit&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((236930, 14), (26326, 14), (236930,), (26326,))
+</pre></div></div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_clusters&quot;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">123</span><span class="p">,</span>
+    <span class="s2">&quot;copy_x&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 7.36 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inertia_opt</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">inertia_</span>
+<span class="n">n_iter_opt</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_iter_</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn inertia: </span><span class="si">{</span><span class="n">inertia_opt</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn number of iterations: </span><span class="si">{</span><span class="n">n_iter_opt</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Intel® extension for Scikit-learn inertia: 13346.641333761074
+Intel® extension for Scikit-learn number of iterations: 274
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class KMeans</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 192.14 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inertia_original</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">inertia_</span>
+<span class="n">n_iter_original</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_iter_</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn inertia: </span><span class="si">{</span><span class="n">inertia_original</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn number of iterations: </span><span class="si">{</span><span class="n">n_iter_original</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Original Scikit-learn inertia: 13352.813785961785
+Original Scikit-learn number of iterations: 212
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare inertia and number of iterations of patched Scikit-learn and original&lt;/h3&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;strong&gt;Inertia:&lt;/strong&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn: </span><span class="si">{</span><span class="n">inertia_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Unpatched Scikit-learn: </span><span class="si">{</span><span class="n">inertia_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Ratio: </span><span class="si">{</span><span class="n">inertia_opt</span><span class="o">/</span><span class="n">inertia_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;strong&gt;Number of iterations:&lt;/strong&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn: </span><span class="si">{</span><span class="n">n_iter_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Unpatched Scikit-learn: </span><span class="si">{</span><span class="n">n_iter_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Ratio: </span><span class="si">{</span><span class="p">(</span><span class="n">n_iter_opt</span><span class="o">/</span><span class="n">n_iter_original</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> &lt;br&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Number of iterations is bigger but algorithm is much faster and inertia is lower&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="ridge_regression.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="dbscan.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
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+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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diff --git a/2025.0/samples/kmeans.ipynb b/2025.0/samples/kmeans.ipynb
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2025.0/samples/kmeans.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/knn_mnist.html b/2025.0/samples/knn_mnist.html
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index 0000000000..951ecb57b2
--- /dev/null
+++ b/2025.0/samples/knn_mnist.html
@@ -0,0 +1,714 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
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+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn KNN for MNIST dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="index" title="Index" href="../genindex.html" />
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+      data-bs-toggle="dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
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+</ul>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn KNN for MNIST dataset</li>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-KNN-for-MNIST-dataset">
+<h1>Intel® Extension for Scikit-learn KNN for MNIST dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-KNN-for-MNIST-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;mnist_784&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">72</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((56000, 784), (14000, 784), (56000,), (14000,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_neighbors&quot;</span><span class="p">:</span> <span class="mi">40</span><span class="p">,</span> <span class="s2">&quot;weights&quot;</span><span class="p">:</span> <span class="s2">&quot;distance&quot;</span><span class="p">,</span> <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">knn</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">predicted</span> <span class="o">=</span> <span class="n">knn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">time_opt</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">time_opt</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 1.45 s&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for Intel® extension for Scikit-learn KNN:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for Intel® extension for Scikit-learn KNN:
+              precision    recall  f1-score   support
+
+           0       0.97      0.99      0.98      1365
+           1       0.93      0.99      0.96      1637
+           2       0.99      0.94      0.96      1401
+           3       0.96      0.95      0.96      1455
+           4       0.98      0.96      0.97      1380
+           5       0.95      0.95      0.95      1219
+           6       0.96      0.99      0.97      1317
+           7       0.94      0.95      0.95      1420
+           8       0.99      0.90      0.94      1379
+           9       0.92      0.94      0.93      1427
+
+    accuracy                           0.96     14000
+   macro avg       0.96      0.96      0.96     14000
+weighted avg       0.96      0.96      0.96     14000
+
+
+</pre></div></div>
+</div>
+<p><em>The first column of the classification report above is the class labels.</em></p>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class KNeighborsClassifier.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
+
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">knn</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">predicted</span> <span class="o">=</span> <span class="n">knn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">time_original</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">time_original</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 36.15 s&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for original Scikit-learn KNN:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for original Scikit-learn KNN:
+              precision    recall  f1-score   support
+
+           0       0.97      0.99      0.98      1365
+           1       0.93      0.99      0.96      1637
+           2       0.99      0.94      0.96      1401
+           3       0.96      0.95      0.96      1455
+           4       0.98      0.96      0.97      1380
+           5       0.95      0.95      0.95      1219
+           6       0.96      0.99      0.97      1317
+           7       0.94      0.95      0.95      1420
+           8       0.99      0.90      0.94      1379
+           9       0.92      0.94      0.93      1427
+
+    accuracy                           0.96     14000
+   macro avg       0.96      0.96      0.96     14000
+weighted avg       0.96      0.96      0.96     14000
+
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h2&gt;With scikit-learn-intelex patching you can:&lt;/h2&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">time_original</span><span class="o">/</span><span class="n">time_opt</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/2025.0/samples/knn_mnist.ipynb b/2025.0/samples/knn_mnist.ipynb
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2025.0/samples/knn_mnist.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/lasso_regression.html b/2025.0/samples/lasso_regression.html
new file mode 100644
index 0000000000..40aa732c9e
--- /dev/null
+++ b/2025.0/samples/lasso_regression.html
@@ -0,0 +1,738 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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+      <link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
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+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
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+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" href="linear_regression.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" href="ElasticNet.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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+      current_version = "2025.0"
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+      data-bs-toggle="dropdown"
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+      aria-controls="version-switcher-dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
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+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/lasso_regression.ipynb.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Lasso-Regression-for-YearPredictionMSD-dataset">
+<h1>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Lasso-Regression-for-YearPredictionMSD-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset_dir</span> <span class="o">=</span> <span class="s2">&quot;data&quot;</span>
+<span class="n">dataset_name</span> <span class="o">=</span> <span class="s2">&quot;year_prediction_msd&quot;</span>
+<span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip&quot;</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">local_url</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">url</span><span class="p">))</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">local_url</span><span class="p">):</span>
+    <span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="s2">&quot;wb+&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">response</span><span class="o">.</span><span class="n">iter_content</span><span class="p">(</span><span class="mi">8192</span><span class="p">):</span>
+            <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="n">year</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((463810, 90), (51535, 90), (463810,), (51535,))
+</pre></div></div>
+</div>
+</section>
+<section id="Normalize-the-data">
+<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
+    <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Lasso</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.06 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 0.9676607251167297&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class Lasso</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Lasso</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 0.83 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Lasso algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 0.9676599502563477&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="ElasticNet.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="linear_regression.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
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+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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diff --git a/2025.0/samples/lasso_regression.ipynb b/2025.0/samples/lasso_regression.ipynb
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2025.0/samples/lasso_regression.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
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+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
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+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
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+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/linear_regression.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Linear-Regression-for-YearPredictionMSD-dataset">
+<h1>Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Linear-Regression-for-YearPredictionMSD-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset_dir</span> <span class="o">=</span> <span class="s2">&quot;data&quot;</span>
+<span class="n">dataset_name</span> <span class="o">=</span> <span class="s2">&quot;year_prediction_msd&quot;</span>
+<span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip&quot;</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">local_url</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">url</span><span class="p">))</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">local_url</span><span class="p">):</span>
+    <span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="s2">&quot;wb+&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">response</span><span class="o">.</span><span class="n">iter_content</span><span class="p">(</span><span class="mi">8192</span><span class="p">):</span>
+            <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="n">year</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((463810, 90), (51535, 90), (463810,), (51535,))
+</pre></div></div>
+</div>
+</section>
+<section id="Normalize-the-data">
+<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.03 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 0.7716818451881409&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class LinearRegression</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 0.53 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Linear Regression algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 0.7716856598854065&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="lasso_regression.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
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diff --git a/2025.0/samples/linear_regression.ipynb b/2025.0/samples/linear_regression.ipynb
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2025.0/samples/linear_regression.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/logistictic_regression_cifar.html b/2025.0/samples/logistictic_regression_cifar.html
new file mode 100644
index 0000000000..f9cb736575
--- /dev/null
+++ b/2025.0/samples/logistictic_regression_cifar.html
@@ -0,0 +1,693 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
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+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn SVC for Adult dataset" href="svc_adult.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="knn_mnist.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
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+      </script>
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+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+          
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+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset</li>
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+            <a href="../_sources/samples/logistictic_regression_cifar.ipynb.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-Logistic-Regression-for-Cifar-dataset">
+<h1>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Logistic-Regression-for-Cifar-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+
+<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;CIFAR-100&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">43</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((54000, 3072), (6000, 3072), (54000,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
+    <span class="s2">&quot;solver&quot;</span><span class="p">:</span> <span class="s2">&quot;lbfgs&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;multi_class&quot;</span><span class="p">:</span> <span class="s2">&quot;multinomial&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 24.82 s&#39;
+</pre></div></div>
+</div>
+<p>Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">log_loss_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">log_loss</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn Log Loss: </span><span class="si">{</span><span class="n">log_loss_opt</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class LogisticRegression</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 395.03 s&#39;
+</pre></div></div>
+</div>
+<p>Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">log_loss_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">log_loss</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn Log Loss: </span><span class="si">{</span><span class="n">log_loss_original</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn Log Loss: 3.7140870590578428 s&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare Log Loss metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Log Loss metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">log_loss_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Log Loss metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">log_loss_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">log_loss_opt</span><span class="o">/</span><span class="n">log_loss_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
+    Cookies
+</a>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
+    Privacy
+</a>
+<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
+    Do Not Share My Personal Information
+</a>
+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
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diff --git a/2025.0/samples/logistictic_regression_cifar.ipynb b/2025.0/samples/logistictic_regression_cifar.ipynb
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2025.0/samples/logistictic_regression_cifar.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/nusvr_medical_charges.html b/2025.0/samples/nusvr_medical_charges.html
new file mode 100644
index 0000000000..35aa1b540b
--- /dev/null
+++ b/2025.0/samples/nusvr_medical_charges.html
@@ -0,0 +1,702 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=132153fd" />
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+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" href="random_forest_yolanda.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" href="linear_regression.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
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+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/nusvr_medical_charges.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-NuSVR-for-Medical-Charges-dataset">
+<h1>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-NuSVR-for-Medical-Charges-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;medical_charges_nominal&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Encode categorical features</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cat_columns</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">([</span><span class="s2">&quot;category&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span>
+<span class="n">x</span><span class="p">[</span><span class="n">cat_columns</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">cat_columns</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">codes</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((48919, 11), (114146, 11), (48919,), (114146,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">NuSVR</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;nu&quot;</span><span class="p">:</span> <span class="mf">0.4</span><span class="p">,</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="n">y_train</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
+    <span class="s2">&quot;degree&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s2">&quot;kernel&quot;</span><span class="p">:</span> <span class="s2">&quot;poly&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">nusvr</span> <span class="o">=</span> <span class="n">NuSVR</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 24.69 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_opt</span> <span class="o">=</span> <span class="n">nusvr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn R2 score: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn R2 score: 0.8635974264586637&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class NuSVR</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">NuSVR</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">nusvr</span> <span class="o">=</span> <span class="n">NuSVR</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 331.85 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the NuSVR algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_original</span> <span class="o">=</span> <span class="n">nusvr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn R2 score: </span><span class="si">{</span><span class="n">score_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn R2 score: 0.8636031741516902&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare R2 score of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;R2 score of patched Scikit-learn: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;R2 score of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">score_opt</span><span class="o">/</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
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+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="linear_regression.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/2025.0/samples/nusvr_medical_charges.ipynb b/2025.0/samples/nusvr_medical_charges.ipynb
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2025.0/samples/nusvr_medical_charges.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/random_forest_yolanda.html b/2025.0/samples/random_forest_yolanda.html
new file mode 100644
index 0000000000..d68a1467e4
--- /dev/null
+++ b/2025.0/samples/random_forest_yolanda.html
@@ -0,0 +1,684 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
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+  <title>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Random Forest for Yolanda dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/random_forest_yolanda.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Random-Forest-for-Yolanda-dataset">
+<h1>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Random-Forest-for-Yolanda-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Yolanda&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">72</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((280000, 100), (120000, 100), (280000,), (120000,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_estimators&quot;</span><span class="p">:</span> <span class="mi">150</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">44</span><span class="p">,</span> <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 42.56 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn Mean Squared Error: </span><span class="si">{</span><span class="n">mse_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class RandomForestRegressor.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 123.34 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Random Forest algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn Mean Squared Error: </span><span class="si">{</span><span class="n">mse_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn Mean Squared Error: 83.62232345666878&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_opt</span><span class="o">/</span><span class="n">mse_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="nusvr_medical_charges.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="ridge_regression.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
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+      class="version-switcher__button"
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diff --git a/2025.0/samples/random_forest_yolanda.ipynb b/2025.0/samples/random_forest_yolanda.ipynb
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2025.0/samples/random_forest_yolanda.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/ridge_regression.html b/2025.0/samples/ridge_regression.html
new file mode 100644
index 0000000000..aede3309f8
--- /dev/null
+++ b/2025.0/samples/ridge_regression.html
@@ -0,0 +1,736 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
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+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
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+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/ridge_regression.ipynb.txt" rel="nofollow"> View page source</a>
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+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Ridge-Regression-for-Airlines-DepDelay-dataset">
+<h1>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Ridge-Regression-for-Airlines-DepDelay-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Airlines_DepDelay_10M&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Let’s encode categorical features with LabelEncoder</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;UniqueCarrier&quot;</span><span class="p">,</span> <span class="s2">&quot;Origin&quot;</span><span class="p">,</span> <span class="s2">&quot;Dest&quot;</span><span class="p">]:</span>
+    <span class="n">le</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+    <span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((9000000, 9), (1000000, 9), (9000000,), (1000000,))
+</pre></div></div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_train</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="mf">0.3</span><span class="p">,</span>
+    <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.06 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 1.0014288520708046&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class Ridge</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 0.70 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Ridge Regression algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 1.0014288520708057&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="random_forest_yolanda.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="kmeans.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+  <hr/>
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+<jinja2.runtime.BlockReference object at 0x7f2777269ba0>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+          SphinxRtdTheme.Navigation.enable(true);
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+</html>
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diff --git a/2025.0/samples/ridge_regression.ipynb b/2025.0/samples/ridge_regression.ipynb
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2025.0/samples/ridge_regression.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/svc_adult.html b/2025.0/samples/svc_adult.html
new file mode 100644
index 0000000000..455315122c
--- /dev/null
+++ b/2025.0/samples/svc_adult.html
@@ -0,0 +1,689 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
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+  <title>Intel® Extension for Scikit-learn SVC for Adult dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="index" title="Index" href="../genindex.html" />
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+    <link rel="next" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" href="ElasticNet.html" />
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+<body class="wy-body-for-nav"> 
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+            Intel(R) Extension for Scikit-learn*
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+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-SVC-for-Adult-dataset">
+<h1>Intel® Extension for Scikit-learn SVC for Adult dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-SVC-for-Adult-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;a9a&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">100.0</span><span class="p">,</span> <span class="s2">&quot;kernel&quot;</span><span class="p">:</span> <span class="s2">&quot;rbf&quot;</span><span class="p">,</span> <span class="s2">&quot;gamma&quot;</span><span class="p">:</span> <span class="s2">&quot;scale&quot;</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 14.08 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for Intel® extension for Scikit-learn SVC:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for Intel® extension for Scikit-learn SVC:
+              precision    recall  f1-score   support
+
+        -1.0       0.87      0.90      0.88      7414
+         1.0       0.64      0.58      0.61      2355
+
+    accuracy                           0.82      9769
+   macro avg       0.76      0.74      0.75      9769
+weighted avg       0.82      0.82      0.82      9769
+
+
+</pre></div></div>
+</div>
+<p><em>The first column of the classification report above is the class labels.</em></p>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class SVC.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the SVC algorithm with original Scikit-learn library for Adult dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 803.06 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the SVC algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for original Scikit-learn SVC:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for original Scikit-learn SVC:
+              precision    recall  f1-score   support
+
+        -1.0       0.87      0.90      0.88      7414
+         1.0       0.64      0.58      0.61      2355
+
+    accuracy                           0.82      9769
+   macro avg       0.76      0.74      0.75      9769
+weighted avg       0.82      0.82      0.82      9769
+
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h2&gt;With scikit-learn-intelex patching you can:&lt;/h2&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="logistictic_regression_cifar.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="ElasticNet.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+          SphinxRtdTheme.Navigation.enable(true);
+      });
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+
+</body>
+</html>
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diff --git a/2025.0/samples/svc_adult.ipynb b/2025.0/samples/svc_adult.ipynb
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2025.0/samples/svc_adult.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/samples/tsne.html b/2025.0/samples/tsne.html
new file mode 100644
index 0000000000..f2817418c8
--- /dev/null
+++ b/2025.0/samples/tsne.html
@@ -0,0 +1,669 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn TSNE example &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="../_static/documentation_options.js?v=0e25812e"></script>
+      <script src="../_static/doctools.js?v=9bcbadda"></script>
+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
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+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Kaggle Kernels" href="../kaggle.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset" href="dbscan.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+      </script>
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+</head>
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+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
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+          
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+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-TSNE-example">
+<h1>Intel® Extension for Scikit-learn TSNE example<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-TSNE-example" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Generate-the-data">
+<h2>Generate the data<a class="headerlink" href="#Generate-the-data" title="Link to this heading"></a></h2>
+<div class="line-block">
+<div class="line">Generate isotropic Gaussian blobs for clustering. With the number of samples: 20k Number of features: 100 Number of blobs: 4 Source:</div>
+<div class="line"><a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html">https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html</a></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">20000</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">TSNE</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_components&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">tsne</span> <span class="o">=</span> <span class="n">TSNE</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+<span class="n">embedding_intelex</span> <span class="o">=</span> <span class="n">tsne</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">time_opt</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">time_opt</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® Extension for scikit-learn. Divergence: </span><span class="si">{</span><span class="n">tsne</span><span class="o">.</span><span class="n">kl_divergence_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Intel® extension for Scikit-learn time: 12.63 s
+Intel® Extension for scikit-learn. Divergence: 4.289110606110757
+</pre></div></div>
+</div>
+<p>### Train the same algorithm with original Scikit-learn In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class TSNE.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training algorithm with original Scikit-learn library for generated dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">TSNE</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_components&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">tsne</span> <span class="o">=</span> <span class="n">TSNE</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+<span class="n">embedding_original</span> <span class="o">=</span> <span class="n">tsne</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">time_original</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">time_original</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn. Divergence: </span><span class="si">{</span><span class="n">tsne</span><span class="o">.</span><span class="n">kl_divergence_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Original Scikit-learn time: 37.66 s
+Original Scikit-learn. Divergence: 4.2955403327941895
+</pre></div></div>
+</div>
+</section>
+<section id="Plot-embeddings-original-scikit-learn-and-Intel®-extension">
+<h2>Plot embeddings original scikit-learn and Intel® extension<a class="headerlink" href="#Plot-embeddings-original-scikit-learn-and-Intel®-extension" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">y</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">emb</span><span class="p">,</span> <span class="n">title</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+    <span class="p">[</span><span class="n">embedding_intelex</span><span class="p">,</span> <span class="n">embedding_original</span><span class="p">],</span>
+    <span class="p">[</span><span class="s2">&quot;Intel® Extension for scikit-learn&quot;</span><span class="p">,</span> <span class="s2">&quot;Original scikit-learn&quot;</span><span class="p">],</span>
+<span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">emb</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">emb</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">colors</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_tsne_15_0.png" src="../_images/samples_tsne_15_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_tsne_15_1.png" src="../_images/samples_tsne_15_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sa">f</span><span class="s2">&quot;Speedup for this run: </span><span class="si">{</span><span class="p">(</span><span class="n">time_original</span><span class="o">/</span><span class="n">time_opt</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Speedup for this run: 3.0&#39;
+</pre></div></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="dbscan.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../kaggle.html" class="btn btn-neutral float-right" title="Kaggle Kernels" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/2025.0/samples/tsne.ipynb b/2025.0/samples/tsne.ipynb
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2025.0/samples/tsne.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.0/search.html b/2025.0/search.html
new file mode 100644
index 0000000000..828f9f002a
--- /dev/null
+++ b/2025.0/search.html
@@ -0,0 +1,498 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Search &mdash; Intel(R) Extension for Scikit-learn* 2025.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
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+
+  
+    <link rel="shortcut icon" href="_static/favicons.png"/>
+    
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="_static/documentation_options.js?v=0e25812e"></script>
+      <script src="_static/doctools.js?v=9bcbadda"></script>
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+      <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="_static/copybutton.js?v=f281be69"></script>
+      <script src="_static/version_switcher.js?v=107152b5"></script>
+      <script src="_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <script src="_static/searchtools.js"></script>
+    <script src="_static/language_data.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="#" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
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+            var url = 'https://www.intel.com/content/dam/www/global/wap/tms-loader.js'; // WAP file URL
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="#" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
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31], "tsne": [7, 32], "tune": 7, "tutori": 34, "type": 9, "u": 3, "unpatch": 18, "us": [2, 10, 11, 13, 21], "usag": 2, "usm_ndarrai": 2, "util": 21, "verbos": 35, "vision": 12, "visual": 21, "workflow": [10, 21], "yearpredictionmsd": [25, 26], "yolanda": 29}})
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+            Intel(R) Extension for Scikit-learn*
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
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+  <section id="intel-r-extension-for-scikit-learn-support">
+<h1>Intel(R) Extension for Scikit-learn Support<a class="headerlink" href="#intel-r-extension-for-scikit-learn-support" title="Link to this heading"></a></h1>
+<p>We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn.</p>
+<p>Use the following methods if you face any challenges.</p>
+<section id="issues">
+<h2>Issues<a class="headerlink" href="#issues" title="Link to this heading"></a></h2>
+<p>If you have a problem, check out the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">GitHub Issues</a> to see if the issue you want to address is already reported.</p>
+<p>You may find users that have encountered the same bug or have similar ideas for changes or updates.</p>
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+            Intel(R) Extension for Scikit-learn*
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+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
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+           <div itemprop="articleBody">
+             
+  <section id="intelex-tutorials-case-studies">
+<h1>Intel® Extension for Scikit-learn* Tutorials &amp; Case Studies<a class="headerlink" href="#intelex-tutorials-case-studies" title="Link to this heading"></a></h1>
+<p>Explore the tutorials &amp; case studies to help you to harness the full potential of Intel® Extension for Scikit-learn*.</p>
+<section id="tutorials">
+<h2>Tutorials<a class="headerlink" href="#tutorials" title="Link to this heading"></a></h2>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
+<div class="sd-row sd-row-cols-3 sd-row-cols-xs-3 sd-row-cols-sm-3 sd-row-cols-md-3 sd-row-cols-lg-3 docutils">
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
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+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Introduction to scikit-learn* Essentials for Machine Learning</div>
+<p class="sd-card-text">An overview of scikit-learn essentials.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/introduction-to-scikit-learn-essentials-for-ml.html"><span>https://www.intel.com/content/www/us/en/developer/videos/introduction-to-scikit-learn-essentials-for-ml.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Advanced scikit-learn* Essentials for Machine Learning</div>
+<p class="sd-card-text">Special technique to perform scikit-learn computation on Intel GPUs.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/advanced-scikit-learn-essentials-for-ml.html"><span>https://www.intel.com/content/www/us/en/developer/videos/advanced-scikit-learn-essentials-for-ml.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Develop Efficient AI Solutions with Accelerated Machine Learning</div>
+<p class="sd-card-text">Techniques for maximizing Intel® Extension for Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/accelerated-machine-learning-for-ai-solutions.html"><span>https://www.intel.com/content/www/us/en/developer/videos/accelerated-machine-learning-for-ai-solutions.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Getting started with classical Machine Learning Frameworks using Google Colaboratory</div>
+<p class="sd-card-text">Simple Installation of Intel® Extension for Scikit-learn* on Google Colaboratory.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Getting-started-with-classical-Machine-Learning-Frameworks-using/post/1450139"><span>https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Getting-started-with-classical-Machine-Learning-Frameworks-using/post/1450139</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Accelerate Machine Learning Workloads: K-means and GPairs Algorithms</div>
+<p class="sd-card-text">An introduction to Intel® Extension for Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/accelerate-ml-workloads-k-means-gpairs-algorithms.html"><span>https://www.intel.com/content/www/us/en/developer/videos/accelerate-ml-workloads-k-means-gpairs-algorithms.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Benchmarking Intel® Extension for Scikit-learn*: How Much Faster Is It?</div>
+<p class="sd-card-text">Explore and compare the performance of the Intel Extension for Scikit-learn* vs. stock Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/technical/benchmarking-intel-extension-for-scikit-learn.html"><span>https://www.intel.com/content/www/us/en/developer/articles/technical/benchmarking-intel-extension-for-scikit-learn.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Drive Innovation &amp; Performance into Your scikit-learn* Machine Learning Tasks</div>
+<p class="sd-card-text">An overview of scikit-learn essentials.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/drive-innovation-performance-scikit-learn-ml-tasks.html"><span>https://www.intel.com/content/www/us/en/developer/videos/drive-innovation-performance-scikit-learn-ml-tasks.html</span></a></div>
+</div>
+</div>
+</div>
+</section>
+<section id="case-studies">
+<h2>Case Studies<a class="headerlink" href="#case-studies" title="Link to this heading"></a></h2>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
+<div class="sd-row sd-row-cols-3 sd-row-cols-xs-3 sd-row-cols-sm-3 sd-row-cols-md-3 sd-row-cols-lg-3 docutils">
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Greener Machine Learning Computing with Intel® AI Acceleration</div>
+<p class="sd-card-text">Lowering the Environmental Impact of AI Workloads by 7x using Intel® Extension for Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/case-study/greener-machine-learning-with-intel-ai.html"><span>https://www.intel.com/content/www/us/en/developer/articles/case-study/greener-machine-learning-with-intel-ai.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Supply Chain Optimization at Enterprise Scale</div>
+<p class="sd-card-text">Leveraging Open-Source AI Technologies with Red Hat OpenShift* Data Science and Intel® Architecture.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/technical/supply-chain-optimization-at-enterprise-scale.html"><span>https://www.intel.com/content/www/us/en/developer/articles/technical/supply-chain-optimization-at-enterprise-scale.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+HippoScreen Improves AI Performance by 2.4x with oneAPI Tools</div>
+<p class="sd-card-text">The neurotechnology startup used Intel® Extension for Scikit-learn* to the improve efficiency and build times of deep-learning models used in its Brain Waves AI system.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html"><span>https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html</span></a></div>
+</div>
+</div>
+</div>
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+<html class="writer-html5" lang="en" data-content_root="./">
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+    <script src="_static/js/theme.js"></script>
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+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Verbose Mode</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/verbose.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="verbose-mode">
+<span id="verbose"></span><h1>Verbose Mode<a class="headerlink" href="#verbose-mode" title="Link to this heading"></a></h1>
+<p>Use Intel® Extension for Scikit-learn* in verbose mode to find out which implementation of the algorithm is currently used,
+Intel® Extension for Scikit-learn* or original Scikit-learn.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Verbose mode is only available for <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">supported algorithms</span></a>.</p>
+</div>
+<p>To enable verbose mode, set the <code class="docutils literal notranslate"><span class="pre">SKLEARNEX_VERBOSE</span></code> environment variable as shown below:</p>
+<ul>
+<li><p>On Linux* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
+</pre></div>
+</div>
+</li>
+<li><p>On Windows* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
+</pre></div>
+</div>
+</li>
+</ul>
+<p>Alternatively, get Intel® Extension for Scikit-learn* logger and set its logging level in the Python code:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span>
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s1">&#39;sklearnex&#39;</span><span class="p">)</span>
+<span class="n">logger</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>During the calls that use Intel-optimized scikit-learn, you will receive additional print statements
+that indicate which implementation is being called.
+These print statements are only available for <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">supported algorithms</span></a>.</p>
+<p>For example, for DBSCAN you get one of these print statements depending on which implementation is used:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">INFO</span><span class="p">:</span><span class="n">sklearnex</span><span class="p">:</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">DBSCAN</span><span class="o">.</span><span class="n">fit</span><span class="p">:</span> <span class="n">running</span> <span class="n">accelerated</span> <span class="n">version</span> <span class="n">on</span> <span class="n">CPU</span>
+</pre></div>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">INFO</span><span class="p">:</span><span class="n">sklearnex</span><span class="p">:</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">DBSCAN</span><span class="o">.</span><span class="n">fit</span><span class="p">:</span> <span class="n">fallback</span> <span class="n">to</span> <span class="n">original</span> <span class="n">Scikit</span><span class="o">-</span><span class="n">learn</span>
+</pre></div>
+</div>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="array_api.html" class="btn btn-neutral float-left" title="Array API support" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="preview.html" class="btn btn-neutral float-right" title="Preview Functionality" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f27c2289ba0>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
+    Cookies
+</a>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
+    Privacy
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+<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
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+</a>
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+  </div>
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+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.0"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
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+# Sphinx build info version 1
+# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 3a319e52f99dc3b89d49d5f4c412bd8e
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb b/2025.1/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/dbscan.ipynb b/2025.1/.doctrees/nbsphinx/samples/dbscan.ipynb
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/dbscan.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/kmeans.ipynb b/2025.1/.doctrees/nbsphinx/samples/kmeans.ipynb
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/kmeans.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/knn_mnist.ipynb b/2025.1/.doctrees/nbsphinx/samples/knn_mnist.ipynb
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/knn_mnist.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/lasso_regression.ipynb b/2025.1/.doctrees/nbsphinx/samples/lasso_regression.ipynb
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/lasso_regression.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/linear_regression.ipynb b/2025.1/.doctrees/nbsphinx/samples/linear_regression.ipynb
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/linear_regression.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb b/2025.1/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb b/2025.1/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb b/2025.1/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/ridge_regression.ipynb b/2025.1/.doctrees/nbsphinx/samples/ridge_regression.ipynb
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/ridge_regression.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/svc_adult.ipynb b/2025.1/.doctrees/nbsphinx/samples/svc_adult.ipynb
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/svc_adult.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/.doctrees/nbsphinx/samples/tsne.ipynb b/2025.1/.doctrees/nbsphinx/samples/tsne.ipynb
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2025.1/.doctrees/nbsphinx/samples/tsne.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#unpatching">Unpatching</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
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+
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+  <h1>All modules for which code is available</h1>
+<ul><li><a href="sklearnex/basic_statistics/basic_statistics.html">sklearnex.basic_statistics.basic_statistics</a></li>
+<li><a href="sklearnex/basic_statistics/incremental_basic_statistics.html">sklearnex.basic_statistics.incremental_basic_statistics</a></li>
+<li><a href="sklearnex/covariance/incremental_covariance.html">sklearnex.covariance.incremental_covariance</a></li>
+<li><a href="sklearnex/linear_model/incremental_linear.html">sklearnex.linear_model.incremental_linear</a></li>
+</ul>
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+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
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+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for sklearnex.basic_statistics.basic_statistics</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2023 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">_check_sample_weight</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.basic_statistics</span> <span class="kn">import</span> <span class="n">BasicStatistics</span> <span class="k">as</span> <span class="n">onedal_BasicStatistics</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">PatchingConditionsChain</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.6&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">validate_data</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">validate_data</span> <span class="o">=</span> <span class="n">BaseEstimator</span><span class="o">.</span><span class="n">_validate_data</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">StrOptions</span>
+
+
+<div class="viewcode-block" id="BasicStatistics">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics">[docs]</a>
+<span class="nd">@control_n_jobs</span><span class="p">(</span><span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;fit&quot;</span><span class="p">])</span>
+<span class="k">class</span> <span class="nc">BasicStatistics</span><span class="p">(</span><span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Estimator for basic statistics.</span>
+<span class="sd">    Allows to compute basic statistics for provided data.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    result_options: string or list, default=&#39;all&#39;</span>
+<span class="sd">        Used to set statistics to calculate. Possible values are ``&#39;min&#39;``, ``&#39;max&#39;``, ``&#39;sum&#39;``, ``&#39;mean&#39;``, ``&#39;variance&#39;``,</span>
+<span class="sd">        ``&#39;variation&#39;``, ``sum_squares&#39;``, ``sum_squares_centered&#39;``, ``&#39;standard_deviation&#39;``, ``&#39;second_order_raw_moment&#39;``</span>
+<span class="sd">        or a list containing any of these values. If set to ``&#39;all&#39;`` then all possible statistics will be</span>
+<span class="sd">        calculated.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">        min_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Minimum of each feature over all samples.</span>
+<span class="sd">        max_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Maximum of each feature over all samples.</span>
+<span class="sd">        sum_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of each feature over all samples.</span>
+<span class="sd">        mean_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Mean of each feature over all samples.</span>
+<span class="sd">        variance_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Variance of each feature over all samples.</span>
+<span class="sd">        variation_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Variation of each feature over all samples.</span>
+<span class="sd">        sum_squares_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of squares for each feature over all samples.</span>
+<span class="sd">        standard_deviation_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Standard deviation of each feature over all samples.</span>
+<span class="sd">        sum_squares_centered_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Centered sum of squares for each feature over all samples.</span>
+<span class="sd">        second_order_raw_moment_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Second order moment of each feature over all samples.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Attribute exists only if corresponding result option has been provided.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Names of attributes without the trailing underscore are</span>
+<span class="sd">    supported currently but deprecated in 2025.1 and will be removed in 2026.0</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Some results can exhibit small variations due to</span>
+<span class="sd">    floating point error accumulation and multithreading.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.basic_statistics import BasicStatistics</span>
+<span class="sd">    &gt;&gt;&gt; bs = BasicStatistics(result_options=[&#39;sum&#39;, &#39;min&#39;, &#39;max&#39;])</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; bs.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; bs.sum_</span>
+<span class="sd">    np.array([4., 6.])</span>
+<span class="sd">    &gt;&gt;&gt; bs.min_</span>
+<span class="sd">    np.array([1., 2.])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">result_options</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">=</span> <span class="n">result_options</span>
+
+    <span class="n">_onedal_basic_statistics</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_BasicStatistics</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="p">[</span>
+                <span class="n">StrOptions</span><span class="p">(</span>
+                    <span class="p">{</span>
+                        <span class="s2">&quot;all&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;min&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;max&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variance&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;standard_deviation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares_centered&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;second_order_raw_moment&quot;</span><span class="p">,</span>
+                    <span class="p">}</span>
+                <span class="p">),</span>
+                <span class="nb">list</span><span class="p">,</span>
+            <span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="nf">_save_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="n">onedal_BasicStatistics</span><span class="o">.</span><span class="n">get_all_result_options</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="nb">setattr</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">result_options</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span><span class="p">,</span>
+                <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="p">,</span> <span class="n">result_options</span><span class="p">),</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">option</span> <span class="ow">in</span> <span class="n">result_options</span><span class="p">:</span>
+                <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">option</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="p">,</span> <span class="n">option</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attr</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="n">onedal_BasicStatistics</span><span class="o">.</span><span class="n">get_all_result_options</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span>
+        <span class="n">is_deprecated_attr</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">attr</span> <span class="o">==</span> <span class="n">result_options</span><span class="p">)</span>
+        <span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">attr</span> <span class="ow">in</span> <span class="n">result_options</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">is_deprecated_attr</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Result attributes without a trailing underscore were deprecated in version 2025.1 and will be removed in 2026.0&quot;</span>
+            <span class="p">)</span>
+            <span class="n">attr</span> <span class="o">+=</span> <span class="s2">&quot;_&quot;</span>
+        <span class="k">if</span> <span class="n">attr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">attr</span><span class="p">]</span>
+
+        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;</span><span class="si">{</span><span class="n">attr</span><span class="si">}</span><span class="s2">&#39;&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearnex.basic_statistics.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span> <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">_check_sample_weight</span><span class="p">(</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span><span class="p">,</span>
+        <span class="p">}</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_basic_statistics</span><span class="p">(</span><span class="o">**</span><span class="n">onedal_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="p">,</span> <span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_save_attributes</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">1</span>
+
+<div class="viewcode-block" id="BasicStatistics.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calculate statistics of X.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Data for compute, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Weights for compute weighted statistics, where ``n_samples`` is the number of samples.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
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+
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-pypi">Install from PyPI</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guide/acceleration.html">Tuning Guide</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#code-style">Code Style</a></li>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../../index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">sklearnex.basic_statistics.incremental_basic_statistics</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for sklearnex.basic_statistics.incremental_basic_statistics</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span><span class="p">,</span> <span class="n">gen_batches</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">_check_sample_weight</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.basic_statistics</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IncrementalBasicStatistics</span> <span class="k">as</span> <span class="n">onedal_IncrementalBasicStatistics</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">PatchingConditionsChain</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">Interval</span><span class="p">,</span> <span class="n">StrOptions</span>
+
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.6&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">validate_data</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">validate_data</span> <span class="o">=</span> <span class="n">BaseEstimator</span><span class="o">.</span><span class="n">_validate_data</span>
+
+
+<div class="viewcode-block" id="IncrementalBasicStatistics">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics">[docs]</a>
+<span class="nd">@control_n_jobs</span><span class="p">(</span><span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span> <span class="s2">&quot;_onedal_finalize_fit&quot;</span><span class="p">])</span>
+<span class="k">class</span> <span class="nc">IncrementalBasicStatistics</span><span class="p">(</span><span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates basic statistics on the given data, allows for computation when the data are split into</span>
+<span class="sd">    batches. The user can use ``partial_fit`` method to provide a single batch of data or use the ``fit`` method to provide</span>
+<span class="sd">    the entire dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    result_options: string or list, default=&#39;all&#39;</span>
+<span class="sd">        List of statistics to compute</span>
+
+<span class="sd">    batch_size : int, default=None</span>
+<span class="sd">        The number of samples to use for each batch. Only used when calling</span>
+<span class="sd">        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``</span>
+<span class="sd">        is inferred from the data and set to ``5 * n_features``.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">        min_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Minimum of each feature over all samples.</span>
+
+<span class="sd">        max_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Maximum of each feature over all samples.</span>
+
+<span class="sd">        sum_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of each feature over all samples.</span>
+
+<span class="sd">        mean_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Mean of each feature over all samples.</span>
+
+<span class="sd">        variance_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Variance of each feature over all samples.</span>
+
+<span class="sd">        variation_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Variation of each feature over all samples.</span>
+
+<span class="sd">        sum_squares_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of squares for each feature over all samples.</span>
+
+<span class="sd">        standard_deviation_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Standard deviation of each feature over all samples.</span>
+
+<span class="sd">        sum_squares_centered_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Centered sum of squares for each feature over all samples.</span>
+
+<span class="sd">        second_order_raw_moment_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Second order moment of each feature over all samples.</span>
+
+<span class="sd">        n_samples_seen_ : int</span>
+<span class="sd">            The number of samples processed by the estimator. Will be reset on</span>
+<span class="sd">            new calls to ``fit``, but increments across ``partial_fit`` calls.</span>
+
+<span class="sd">        batch_size_ : int</span>
+<span class="sd">            Inferred batch size from ``batch_size``.</span>
+
+<span class="sd">        n_features_in_ : int</span>
+<span class="sd">            Number of features seen during ``fit`` or  ``partial_fit``.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Attribute exists only if corresponding result option has been provided.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Names of attributes without the trailing underscore are</span>
+<span class="sd">    supported currently but deprecated in 2025.1 and will be removed in 2026.0</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.basic_statistics import IncrementalBasicStatistics</span>
+<span class="sd">    &gt;&gt;&gt; incbs = IncrementalBasicStatistics(batch_size=1)</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.partial_fit(X[:1])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.partial_fit(X[1:])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.sum_</span>
+<span class="sd">    np.array([4., 6.])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.min_</span>
+<span class="sd">    np.array([1., 2.])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; incbs.sum_</span>
+<span class="sd">    np.array([4., 6.])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.max_</span>
+<span class="sd">    np.array([3., 4.])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_onedal_incremental_basic_statistics</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_IncrementalBasicStatistics</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="p">[</span>
+                <span class="n">StrOptions</span><span class="p">(</span>
+                    <span class="p">{</span>
+                        <span class="s2">&quot;all&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;min&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;max&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variance&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;standard_deviation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares_centered&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;second_order_raw_moment&quot;</span><span class="p">,</span>
+                    <span class="p">}</span>
+                <span class="p">),</span>
+                <span class="nb">list</span><span class="p">,</span>
+            <span class="p">],</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">result_options</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">result_options</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_basic_statistics</span><span class="o">.</span><span class="n">get_all_result_options</span><span class="p">()</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">=</span> <span class="n">result_options</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearn.basic_statistics.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="k">def</span> <span class="nf">_get_onedal_result_options</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">options</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">onedal_options</span> <span class="o">=</span> <span class="s2">&quot;|&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">result_options</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">onedal_options</span> <span class="o">=</span> <span class="n">options</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">onedal_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">options</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_finalize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+        <span class="n">first_pass</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;n_samples_seen_&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">==</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">check_input</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="p">,</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">reset</span><span class="o">=</span><span class="n">first_pass</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">_check_sample_weight</span><span class="p">(</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">first_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">+=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_onedal_result_options</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">result_options</span><span class="p">)</span>
+        <span class="p">}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_basic_statistics</span><span class="p">(</span>
+                <span class="o">**</span><span class="n">onedal_params</span>
+            <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">_check_sample_weight</span><span class="p">(</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+
+        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">n_features</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">gen_batches</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span><span class="p">):</span>
+            <span class="n">X_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span>
+            <span class="n">weights_batch</span> <span class="o">=</span> <span class="n">sample_weight</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span> <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">(</span>
+                <span class="n">X_batch</span><span class="p">,</span> <span class="n">weights_batch</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attr</span><span class="p">):</span>
+        <span class="n">result_options</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;result_options&quot;</span><span class="p">]</span>
+        <span class="n">sattr</span> <span class="o">=</span> <span class="n">attr</span><span class="o">.</span><span class="n">removesuffix</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)</span>
+        <span class="n">is_statistic_attr</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">sattr</span> <span class="o">==</span> <span class="n">result_options</span><span class="p">)</span>
+        <span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">sattr</span> <span class="ow">in</span> <span class="n">result_options</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">is_statistic_attr</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">sattr</span> <span class="o">==</span> <span class="n">attr</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                    <span class="s2">&quot;Result attributes without a trailing underscore were deprecated in version 2025.1 and will be removed in 2026.0&quot;</span>
+                <span class="p">)</span>
+            <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="p">,</span> <span class="n">sattr</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">attr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">attr</span><span class="p">]</span>
+
+        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;</span><span class="si">{</span><span class="n">attr</span><span class="si">}</span><span class="s2">&#39;&quot;</span>
+        <span class="p">)</span>
+
+<div class="viewcode-block" id="IncrementalBasicStatistics.partial_fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Incremental fit with X. All of X is processed as a single batch.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Data for compute, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Weights for compute weighted statistics, where ``n_samples`` is the number of samples.</span>
+
+<span class="sd">        check_input : bool, default=True</span>
+<span class="sd">            Run ``check_array`` on X.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="p">,</span>
+            <span class="n">check_input</span><span class="o">=</span><span class="n">check_input</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="IncrementalBasicStatistics.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calculate statistics of X using minibatches of size batch_size.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Data for compute, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Weights for compute weighted statistics, where ``n_samples`` is the number of samples.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f04b68e4350>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+   
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+        </div>
+      </div>
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+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
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+
+</body>
+</html>
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diff --git a/2025.1/_modules/sklearnex/covariance/incremental_covariance.html b/2025.1/_modules/sklearnex/covariance/incremental_covariance.html
new file mode 100644
index 0000000000..88beb8a113
--- /dev/null
+++ b/2025.1/_modules/sklearnex/covariance/incremental_covariance.html
@@ -0,0 +1,886 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>sklearnex.covariance.incremental_covariance &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
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+      <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+      <script src="../../../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
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+            var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = url;
+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
+          })();
+      }
+      </script>
+
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../../../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
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+</ul>
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+      <li class="breadcrumb-item active">sklearnex.covariance.incremental_covariance</li>
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+      </li>
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+             
+  <h1>Source code for sklearnex.covariance.incremental_covariance</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ===============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ===============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">linalg</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">clone</span>
+<span class="kn">from</span> <span class="nn">sklearn.covariance</span> <span class="kn">import</span> <span class="n">EmpiricalCovariance</span> <span class="k">as</span> <span class="n">_sklearn_EmpiricalCovariance</span>
+<span class="kn">from</span> <span class="nn">sklearn.covariance</span> <span class="kn">import</span> <span class="n">log_likelihood</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span><span class="p">,</span> <span class="n">gen_batches</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">_num_features</span><span class="p">,</span> <span class="n">check_is_fitted</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">daal_check_version</span><span class="p">,</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.covariance</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IncrementalEmpiricalCovariance</span> <span class="k">as</span> <span class="n">onedal_IncrementalEmpiricalCovariance</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">config_context</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span><span class="p">,</span> <span class="n">wrap_output_data</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">PatchingConditionsChain</span><span class="p">,</span> <span class="n">register_hyperparameters</span>
+<span class="kn">from</span> <span class="nn">..metrics</span> <span class="kn">import</span> <span class="n">pairwise_distances</span>
+<span class="kn">from</span> <span class="nn">..utils._array_api</span> <span class="kn">import</span> <span class="n">get_namespace</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">Interval</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.6&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">validate_data</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">validate_data</span> <span class="o">=</span> <span class="n">BaseEstimator</span><span class="o">.</span><span class="n">_validate_data</span>
+
+
+<div class="viewcode-block" id="IncrementalEmpiricalCovariance">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance">[docs]</a>
+<span class="nd">@control_n_jobs</span><span class="p">(</span><span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span> <span class="s2">&quot;fit&quot;</span><span class="p">,</span> <span class="s2">&quot;_onedal_finalize_fit&quot;</span><span class="p">])</span>
+<span class="k">class</span> <span class="nc">IncrementalEmpiricalCovariance</span><span class="p">(</span><span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Maximum likelihood covariance estimator that allows for the estimation when the data are split into</span>
+<span class="sd">    batches. The user can use the ``partial_fit`` method to provide a single batch of data or use the ``fit`` method to provide</span>
+<span class="sd">    the entire dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    store_precision : bool, default=False</span>
+<span class="sd">        Specifies if the estimated precision is stored.</span>
+
+<span class="sd">    assume_centered : bool, default=False</span>
+<span class="sd">        If True, data are not centered before computation.</span>
+<span class="sd">        Useful when working with data whose mean is almost, but not exactly</span>
+<span class="sd">        zero.</span>
+<span class="sd">        If False (default), data are centered before computation.</span>
+
+<span class="sd">    batch_size : int, default=None</span>
+<span class="sd">        The number of samples to use for each batch. Only used when calling</span>
+<span class="sd">        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``</span>
+<span class="sd">        is inferred from the data and set to ``5 * n_features``, to provide a</span>
+<span class="sd">        balance between approximation accuracy and memory consumption.</span>
+
+<span class="sd">    copy : bool, default=True</span>
+<span class="sd">        If False, X will be overwritten. ``copy=False`` can be used to</span>
+<span class="sd">        save memory but is unsafe for general use.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    location_ : ndarray of shape (n_features,)</span>
+<span class="sd">        Estimated location, i.e. the estimated mean.</span>
+
+<span class="sd">    covariance_ : ndarray of shape (n_features, n_features)</span>
+<span class="sd">        Estimated covariance matrix</span>
+
+<span class="sd">    n_samples_seen_ : int</span>
+<span class="sd">        The number of samples processed by the estimator. Will be reset on</span>
+<span class="sd">        new calls to ``fit``, but increments across ``partial_fit`` calls.</span>
+
+<span class="sd">    batch_size_ : int</span>
+<span class="sd">        Inferred batch size from ``batch_size``.</span>
+
+<span class="sd">    n_features_in_ : int</span>
+<span class="sd">        Number of features seen during ``fit`` or ``partial_fit``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.covariance import IncrementalEmpiricalCovariance</span>
+<span class="sd">    &gt;&gt;&gt; inccov = IncrementalEmpiricalCovariance(batch_size=1)</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.partial_fit(X[:1])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.partial_fit(X[1:])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.covariance_</span>
+<span class="sd">    np.array([[1., 1.],[1., 1.]])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.location_</span>
+<span class="sd">    np.array([2., 3.])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; inccov.covariance_</span>
+<span class="sd">    np.array([[1., 1.],[1., 1.]])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.location_</span>
+<span class="sd">    np.array([2., 3.])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_onedal_incremental_covariance</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_IncrementalEmpiricalCovariance</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;store_precision&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;assume_centered&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+            <span class="s2">&quot;copy&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="n">get_precision</span> <span class="o">=</span> <span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">get_precision</span>
+    <span class="n">error_norm</span> <span class="o">=</span> <span class="n">wrap_output_data</span><span class="p">(</span><span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">error_norm</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">store_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">assume_centered</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">assume_centered</span> <span class="o">=</span> <span class="n">assume_centered</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">store_precision</span> <span class="o">=</span> <span class="n">store_precision</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">copy</span> <span class="o">=</span> <span class="n">copy</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearn.covariance.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_finalize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">daal_check_version</span><span class="p">((</span><span class="mi">2024</span><span class="p">,</span> <span class="s2">&quot;P&quot;</span><span class="p">,</span> <span class="mi">400</span><span class="p">))</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">assume_centered</span><span class="p">:</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">location_</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">covariance_</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">location</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">location</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">location_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">location</span><span class="p">))</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_precision</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">precision_</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">pinvh</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">covariance_</span><span class="p">,</span> <span class="n">check_finite</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">precision_</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">covariance_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">covariance_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;covariance_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">location_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">location_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;location_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+
+        <span class="n">first_pass</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;n_samples_seen_&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">==</span> <span class="mi">0</span>
+
+        <span class="c1"># finite check occurs on onedal side</span>
+        <span class="k">if</span> <span class="n">check_input</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="p">,</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">reset</span><span class="o">=</span><span class="n">first_pass</span><span class="p">,</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;method&quot;</span><span class="p">:</span> <span class="s2">&quot;dense&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;bias&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+            <span class="s2">&quot;assume_centered&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">assume_centered</span><span class="p">,</span>
+        <span class="p">}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_covariance</span><span class="p">(</span><span class="o">**</span><span class="n">onedal_params</span><span class="p">)</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">first_pass</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">+=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="k">finally</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="nd">@wrap_output_data</span>
+    <span class="k">def</span> <span class="nf">score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">xp</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">get_namespace</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+
+        <span class="n">check_is_fitted</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">location_</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">X_test</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X_test</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;numpy&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">xp</span><span class="o">.</span><span class="vm">__name__</span><span class="p">:</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="n">xp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X_test</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+            <span class="c1"># depending on the sklearn version, check_array</span>
+            <span class="c1"># and validate_data will return only numpy arrays</span>
+            <span class="c1"># which will break dpnp/dpctl support. If the</span>
+            <span class="c1"># array namespace isn&#39;t from numpy and the data</span>
+            <span class="c1"># is now a numpy array, it has been validated and</span>
+            <span class="c1"># the original can be used.</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">X_test</span>
+
+        <span class="n">est</span> <span class="o">=</span> <span class="n">clone</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">est</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s2">&quot;assume_centered&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span>
+
+        <span class="c1"># test_cov is a numpy array, but calculated on device</span>
+        <span class="n">test_cov</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span> <span class="o">-</span> <span class="n">location</span><span class="p">)</span><span class="o">.</span><span class="n">covariance_</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">log_likelihood</span><span class="p">(</span><span class="n">test_cov</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_precision</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="n">res</span>
+
+<div class="viewcode-block" id="IncrementalEmpiricalCovariance.partial_fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Incremental fit with X. All of X is processed as a single batch.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where `n_samples` is the number of samples and</span>
+<span class="sd">            `n_features` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        check_input : bool, default=True</span>
+<span class="sd">            Run check_array on X.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">check_input</span><span class="o">=</span><span class="n">check_input</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="IncrementalEmpiricalCovariance.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fit the model with X, using minibatches of size batch_size.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where `n_samples` is the number of samples and</span>
+<span class="sd">            `n_features` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="c1"># finite check occurs on onedal side</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span>
+                <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span> <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span> <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="k">else</span> <span class="mi">5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span>
+
+        <span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Only one sample available. You may want to reshape your data array&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">gen_batches</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span><span class="p">):</span>
+            <span class="n">X_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">(</span><span class="n">X_batch</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="c1"># expose sklearnex pairwise_distances if mahalanobis distance eventually supported</span>
+    <span class="k">def</span> <span class="nf">mahalanobis</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_check_feature_names</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">xp</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">get_namespace</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">precision</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_precision</span><span class="p">()</span>
+        <span class="c1"># compute mahalanobis distances</span>
+        <span class="c1"># pairwise_distances will check n_features (via n_feature matching with</span>
+        <span class="c1"># self.location_) , and will check for finiteness via check array</span>
+        <span class="c1"># check_feature_names will match _validate_data functionally</span>
+        <span class="n">location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">location_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span>
+        <span class="k">if</span> <span class="s2">&quot;numpy&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">xp</span><span class="o">.</span><span class="vm">__name__</span><span class="p">:</span>
+            <span class="c1"># Guarantee that inputs to pairwise_distances match in type and location</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="n">xp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">pairwise_distances</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">location</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;mahalanobis&quot;</span><span class="p">,</span> <span class="n">VI</span><span class="o">=</span><span class="n">precision</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="c1"># Throw the expected sklearn error in an n_feature length violation</span>
+            <span class="k">if</span> <span class="s2">&quot;Incompatible dimension for X and Y matrices: X.shape[1] ==&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;X has </span><span class="si">{</span><span class="n">_num_features</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="si">}</span><span class="s2"> features, but </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;is expecting </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span><span class="si">}</span><span class="s2"> features as input.&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="n">e</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">xp</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dist</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,)))</span> <span class="o">**</span> <span class="mi">2</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="n">mahalanobis</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">mahalanobis</span><span class="o">.</span><span class="vm">__doc__</span>
+    <span class="n">error_norm</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">error_norm</span><span class="o">.</span><span class="vm">__doc__</span>
+    <span class="n">score</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">score</span><span class="o">.</span><span class="vm">__doc__</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f04b79996a0>
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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diff --git a/2025.1/_modules/sklearnex/linear_model/incremental_linear.html b/2025.1/_modules/sklearnex/linear_model/incremental_linear.html
new file mode 100644
index 0000000000..ee286a3205
--- /dev/null
+++ b/2025.1/_modules/sklearnex/linear_model/incremental_linear.html
@@ -0,0 +1,973 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>sklearnex.linear_model.incremental_linear &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
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+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
+          })();
+      }
+      </script>
+
+</head>
+
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+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../../../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
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+      current_version = "2025.1"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../verbose.html">Verbose Mode</a></li>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+</ul>
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+      <li class="breadcrumb-item active">sklearnex.linear_model.incremental_linear</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for sklearnex.linear_model.incremental_linear</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ===============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ===============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">MultiOutputMixin</span><span class="p">,</span> <span class="n">RegressorMixin</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">r2_score</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span><span class="p">,</span> <span class="n">gen_batches</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">check_is_fitted</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.linear_model</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IncrementalLinearRegression</span> <span class="k">as</span> <span class="n">onedal_IncrementalLinearRegression</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">Interval</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.6&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">validate_data</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">validate_data</span> <span class="o">=</span> <span class="n">BaseEstimator</span><span class="o">.</span><span class="n">_validate_data</span>
+
+<span class="kn">from</span> <span class="nn">onedal.common.hyperparameters</span> <span class="kn">import</span> <span class="n">get_hyperparameters</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span><span class="p">,</span> <span class="n">wrap_output_data</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">PatchingConditionsChain</span><span class="p">,</span> <span class="n">register_hyperparameters</span>
+
+
+<div class="viewcode-block" id="IncrementalLinearRegression">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression">[docs]</a>
+<span class="nd">@register_hyperparameters</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;fit&quot;</span><span class="p">:</span> <span class="n">get_hyperparameters</span><span class="p">(</span><span class="s2">&quot;linear_regression&quot;</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">),</span>
+        <span class="s2">&quot;partial_fit&quot;</span><span class="p">:</span> <span class="n">get_hyperparameters</span><span class="p">(</span><span class="s2">&quot;linear_regression&quot;</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">),</span>
+    <span class="p">}</span>
+<span class="p">)</span>
+<span class="nd">@control_n_jobs</span><span class="p">(</span>
+    <span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;fit&quot;</span><span class="p">,</span> <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span> <span class="s2">&quot;predict&quot;</span><span class="p">,</span> <span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="s2">&quot;_onedal_finalize_fit&quot;</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="k">class</span> <span class="nc">IncrementalLinearRegression</span><span class="p">(</span>
+    <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">MultiOutputMixin</span><span class="p">,</span> <span class="n">RegressorMixin</span><span class="p">,</span> <span class="n">BaseEstimator</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Trains a linear regression model, allows for computation if the data are split into</span>
+<span class="sd">    batches. The user can use the ``partial_fit`` method to provide a single batch of data or use the ``fit`` method to provide</span>
+<span class="sd">    the entire dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    fit_intercept : bool, default=True</span>
+<span class="sd">    Whether to calculate the intercept for this model. If set</span>
+<span class="sd">    to False, no intercept will be used in calculations</span>
+<span class="sd">    (i.e. data is expected to be centered).</span>
+
+<span class="sd">    copy_X : bool, default=True</span>
+<span class="sd">        If True, X will be copied; else, it may be overwritten.</span>
+
+<span class="sd">    n_jobs : int, default=None</span>
+<span class="sd">        The number of jobs to use for the computation.</span>
+
+<span class="sd">    batch_size : int, default=None</span>
+<span class="sd">        The number of samples to use for each batch. Only used when calling</span>
+<span class="sd">        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``</span>
+<span class="sd">        is inferred from the data and set to ``5 * n_features``.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    coef_ : array of shape (n_features, ) or (n_targets, n_features)</span>
+<span class="sd">        Estimated coefficients for the linear regression problem.</span>
+<span class="sd">        If multiple targets are passed during the fit (y 2D), this</span>
+<span class="sd">        is a 2D array of shape (n_targets, n_features), while if only</span>
+<span class="sd">        one target is passed, this is a 1D array of length n_features.</span>
+
+<span class="sd">    intercept_ : float or array of shape (n_targets,)</span>
+<span class="sd">        Independent term in the linear model. Set to 0.0 if</span>
+<span class="sd">        `fit_intercept = False`.</span>
+
+<span class="sd">    n_samples_seen_ : int</span>
+<span class="sd">        The number of samples processed by the estimator. Will be reset on</span>
+<span class="sd">        new calls to ``fit``, but increments across ``partial_fit`` calls.</span>
+<span class="sd">        It should be not less than `n_features_in_` if `fit_intercept`</span>
+<span class="sd">        is False and not less than `n_features_in_` + 1 if `fit_intercept`</span>
+<span class="sd">        is True to obtain regression coefficients.</span>
+
+<span class="sd">    batch_size_ : int</span>
+<span class="sd">        Inferred batch size from ``batch_size``.</span>
+
+<span class="sd">    n_features_in_ : int</span>
+<span class="sd">        Number of features seen during ``fit`` or ``partial_fit``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.linear_model import IncrementalLinearRegression</span>
+<span class="sd">    &gt;&gt;&gt; inclr = IncrementalLinearRegression(batch_size=2)</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4], [5, 6], [7, 10]])</span>
+<span class="sd">    &gt;&gt;&gt; y = np.array([1.5, 3.5, 5.5, 8.5])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.partial_fit(X[:2], y[:2])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.partial_fit(X[2:], y[2:])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.coef_</span>
+<span class="sd">    np.array([0.5., 0.5.])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.intercept_</span>
+<span class="sd">    np.array(0.)</span>
+<span class="sd">    &gt;&gt;&gt; inclr.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; inclr.coef_</span>
+<span class="sd">    np.array([0.5., 0.5.])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.intercept_</span>
+<span class="sd">    np.array(0.)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_onedal_incremental_linear</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_IncrementalLinearRegression</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">fit_intercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">copy_X</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span> <span class="o">=</span> <span class="n">fit_intercept</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span> <span class="o">=</span> <span class="n">copy_X</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearn.linear_model.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">r2_score</span><span class="p">(</span>
+            <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_predict</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">),</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">first_pass</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;n_samples_seen_&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">==</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">check_input</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+                <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="p">,</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">y</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">reset</span><span class="o">=</span><span class="n">first_pass</span><span class="p">,</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                    <span class="n">multi_output</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">y</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">first_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">+=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span><span class="p">,</span> <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_linear</span><span class="p">(</span><span class="o">**</span><span class="n">onedal_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_finalize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="n">is_underdetermined</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="n">is_underdetermined</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Not enough samples to finalize&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">y</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                <span class="n">multi_output</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">y</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="n">is_underdetermined</span> <span class="o">=</span> <span class="n">n_samples</span> <span class="o">&lt;</span> <span class="n">n_features</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">is_underdetermined</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Not enough samples to run oneDAL backend&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">n_features</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">gen_batches</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span><span class="p">):</span>
+            <span class="n">X_batch</span><span class="p">,</span> <span class="n">y_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">batch</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">(</span><span class="n">X_batch</span><span class="p">,</span> <span class="n">y_batch</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="c1"># finite check occurs on onedal side</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">n_features</span>
+
+        <span class="k">if</span> <span class="n">n_samples</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Only one sample available. You may want to reshape your data array&quot;</span>
+            <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">intercept_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">intercept_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;intercept_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="nd">@intercept_</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">intercept_</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;intercept_&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">intercept_</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_onedal_model</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">coef_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">coef_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;coef_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="nd">@coef_</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">coef_</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;coef_&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">coef_</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_onedal_model</span>
+
+<div class="viewcode-block" id="IncrementalLinearRegression.partial_fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Incremental fit linear model with X and y. All of X and y is</span>
+<span class="sd">        processed as a single batch.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            `n_features` is the number of features.</span>
+
+<span class="sd">        y : array-like of shape (n_samples,) or (n_samples, n_targets)</span>
+<span class="sd">            Target values, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_targets`` is the number of targets.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">y</span><span class="p">,</span>
+            <span class="n">check_input</span><span class="o">=</span><span class="n">check_input</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="IncrementalLinearRegression.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fit the model with X and y, using minibatches of size ``batch_size``.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features. It is necessary for</span>
+<span class="sd">            ``n_samples`` to be not less than ``n_features`` if ``fit_intercept``</span>
+<span class="sd">            is False and not less than ``n_features + 1`` if ``fit_intercept``</span>
+<span class="sd">            is True</span>
+
+<span class="sd">        y : array-like of shape (n_samples,) or (n_samples, n_targets)</span>
+<span class="sd">            Target values, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_targets`` is the number of targets.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">y</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="IncrementalLinearRegression.predict">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict">[docs]</a>
+    <span class="nd">@wrap_output_data</span>
+    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Predict using the linear model.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like or sparse matrix, shape (n_samples, n_features)</span>
+<span class="sd">            Samples.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        C : array, shape (n_samples, n_targets)</span>
+<span class="sd">            Returns predicted values.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">check_is_fitted</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;predict&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_predict</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="nd">@wrap_output_data</span>
+    <span class="k">def</span> <span class="nf">score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the coefficient of determination of the prediction.</span>
+
+<span class="sd">        The coefficient of determination :math:`R^2` is defined as</span>
+<span class="sd">        :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual</span>
+<span class="sd">        sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`</span>
+<span class="sd">        is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.</span>
+<span class="sd">        The best possible score is 1.0 and it can be negative (because the</span>
+<span class="sd">        model can be arbitrarily worse). A constant model that always predicts</span>
+<span class="sd">        the expected value of `y`, disregarding the input features, would get</span>
+<span class="sd">        a :math:`R^2` score of 0.0.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Test samples. For some estimators this may be a precomputed</span>
+<span class="sd">            kernel matrix or a list of generic objects instead with shape</span>
+<span class="sd">            ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``</span>
+<span class="sd">            is the number of samples used in the fitting for the estimator.</span>
+
+<span class="sd">        y : array-like of shape (n_samples,) or (n_samples, n_outputs)</span>
+<span class="sd">            True values for `X`.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Sample weights.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        score : float</span>
+<span class="sd">            :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        The :math:`R^2` score used when calling ``score`` on a regressor uses</span>
+<span class="sd">        ``multioutput=&#39;uniform_average&#39;`` from version 0.23 to keep consistent</span>
+<span class="sd">        with default value of :func:`~sklearn.metrics.r2_score`.</span>
+<span class="sd">        This influences the ``score`` method of all the multioutput</span>
+<span class="sd">        regressors (except for</span>
+<span class="sd">        :class:`~sklearn.multioutput.MultiOutputRegressor`).</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">check_is_fitted</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;score&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_score</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">y</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f04b63847d0>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
+    Cookies
+</a>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
+    Privacy
+</a>
+<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
+    Do Not Share My Personal Information
+</a>
+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/2025.1/_sources/404.rst.txt b/2025.1/_sources/404.rst.txt
new file mode 100644
index 0000000000..3060a3f9cc
--- /dev/null
+++ b/2025.1/_sources/404.rst.txt
@@ -0,0 +1,28 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+:orphan:
+:nosearch:
+
+Page not found
+==============
+
+Unfortunately, we could not find the page you were looking for. Try:
+
+- using :ref:`search <search>` to browse documentation
+- checking the table of contents on the left
+- filing an `issue <https://github.com/intel/scikit-learn-intelex/issues>`_ or
+  starting a `discussion <https://github.com/intel/scikit-learn-intelex/discussions>`_ on GitHub
\ No newline at end of file
diff --git a/2025.1/_sources/algorithms.rst.txt b/2025.1/_sources/algorithms.rst.txt
new file mode 100644
index 0000000000..4d165c1910
--- /dev/null
+++ b/2025.1/_sources/algorithms.rst.txt
@@ -0,0 +1,543 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _sklearn_algorithms:
+
+####################
+Supported Algorithms
+####################
+
+Applying |intelex| impacts the following scikit-learn algorithms:
+
+on CPU
+------
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVC`
+     - All parameters are supported
+     - No limitations
+   * - `NuSVC`
+     - All parameters are supported
+     - No limitations
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - 
+       - For ``algorithm`` == `'kd_tree'`:
+       
+         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - For ``algorithm`` == `'brute'`:
+         
+         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Multi-output and sparse data are not supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` not in [`'lbfgs'`, `'newton-cg'`]
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVR`
+     - All parameters are supported
+     - No limitations
+   * - `NuSVR`
+     - All parameters are supported
+     - No limitations
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Multi-output and sparse data are not supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported.
+   * - `Ridge`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``solver`` != `'auto'`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported, `#observations` should be >= `#features`.
+   * - `ElasticNet`
+     - All parameters are supported except:
+
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.
+   * - `Lasso`
+     - All parameters are supported except:
+
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported, `#observations` should be >= `#features`.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+     - No limitations
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality Reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+     - Sparse data is not supported
+   * - `IncrementalPCA`
+     - All parameters are supported
+     - Sparse data is not supported
+   * - `TSNE`
+     - All parameters are supported except:
+
+       - ``metric`` != 'euclidean' or `'minkowski'` with ``p`` != `2`
+
+       Refer to :ref:`TSNE acceleration details <acceleration_tsne>` to learn more.
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - 
+       - For ``algorithm`` == 'kd_tree':
+         
+         all parameters except ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+       - For ``algorithm`` == 'brute':
+         
+         all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Sparse data is not supported
+
+Other Tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `EmpiricalCovariance`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `train_test_split`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `assert_all_finite`
+     - All parameters are supported
+     - Only dense data is supported
+   * - `pairwise_distance`
+     - All parameters are supported except:
+     
+       - ``metric`` not in [`'cosine'`, `'correlation'`]
+     - Only dense data is supported
+   * - `roc_auc_score`
+     - All parameters are supported except:
+       
+       - ``average`` != `None`
+       - ``sample_weight`` != `None`
+       - ``max_fpr`` != `None`
+       - ``multi_class`` != `None`
+     - No limitations
+
+on GPU
+------
+
+.. seealso:: :ref:`oneapi_gpu`
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `SVC`
+     - All parameters are supported except:
+
+       - ``kernel`` = `'sigmoid_poly'`
+       - ``class_weight`` != `None`
+     - Only binary dense data is supported
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` != `'newton-cg'`
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+       - ``penalty`` != `'l2'`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Only dense data is supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+       - ``Init`` = `'k-means++'` fallbacks to CPU.
+     - Sparse data is not supported
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality Reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+     
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+
+Other Tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `EmpiricalCovariance`
+     - All parameters are supported
+     - Only dense data is supported
+
+SPMD Support
+------------
+
+.. seealso:: :ref:`distributed`
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+       - ``predict_proba`` method not supported
+     - Only dense data is supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` != `'newton-cg'`
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+       - ``penalty`` != `'l2'`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Only dense data is supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+       - ``Init`` = `'k-means++'` fallbacks to CPU.
+     - Sparse data is not supported
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality Reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+     
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+       - ``fit`` is the only method supported
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+
+Other Tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `EmpiricalCovariance`
+     - All parameters are supported
+     - Only dense data is supported
+
+Scikit-learn Tests
+------------------
+
+Monkey-patched scikit-learn classes and functions passes scikit-learn's own test
+suite, with few exceptions, specified in `deselected_tests.yaml
+<https://github.com/intel/scikit-learn-intelex/blob/main/deselected_tests.yaml>`__.
diff --git a/2025.1/_sources/array_api.rst.txt b/2025.1/_sources/array_api.rst.txt
new file mode 100644
index 0000000000..4c71e0cb88
--- /dev/null
+++ b/2025.1/_sources/array_api.rst.txt
@@ -0,0 +1,123 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _array_api:
+
+=================
+Array API support
+=================
+The `Array API <https://data-apis.org/array-api/latest/>`_ specification defines
+a standard API for all array manipulation libraries with a NumPy-like API.
+Intel(R) Extension for Scikit-Learn doesn't require
+`array-api-compat <https://github.com/data-apis/array-api-compat>`__ to be installed for
+functional support of the array API standard.
+In the current implementation, the functional support of array api follows the functional
+support of different array or DataFrame inputs and does not modify the precision of the
+input and output data formats unless necessary. Any array API input will be converted to host
+numpy.ndarrays and all internal manipulations with data will be done with these representations of
+the input data. DPNP's 'ndarray' and Data Parallel Control's 'usm_ndarray' have special handling
+requirements that are described in the relevant section of this document. Output values will in
+all relevant cases match the input data format.
+
+.. note::
+    Currently, only `array-api-strict <https://github.com/data-apis/array-api-strict>`__,
+    `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__, `dpnp <https://github.com/IntelPython/dpnp>`__
+    and `numpy <https://numpy.org/>`__ are known to work with sklearnex estimators.
+.. note::
+    Stock Scikit-learn’s array API support requires `array-api-compat <https://github.com/data-apis/array-api-compat>`__ to be installed.
+
+
+Support for DPNP and DPCTL
+==========================
+The functional support of input data for sklearnex estimators also extended for SYCL USM array types.
+These include SYCL USM arrays `dpnp's <https://github.com/IntelPython/dpnp>`__ ndarray and
+`Data Parallel Control usm_ndarray <https://intelpython.github.io/dpctl/latest/index.html>`__.
+DPNP ndarray and Data Parallel Control usm_ndarray contain SYCL contexts which can be used for
+`sklearnex` device offloading.
+
+.. note::
+    Current support for DPNP and DPCTL usm_ndarray data can be copied and moved to and from device in sklearnex and have
+    impacts on memory utilization.
+
+DPCTL or DPNP inputs are not required to use `config_context(target_offload=device)`.
+`sklearnex` will use input usm_ndarray sycl context for device offloading.
+
+.. note::
+    As DPCTL or DPNP inputs contain SYCL contexts, they do not require `config_context(target_offload=device)`.
+    However, the use of `config_context`` will override the contained SYCL context and will force movement
+    of data to the targeted device.
+
+
+Support for Array API-compatible inputs
+=======================================
+All patched estimators, metrics, tools and non-scikit-learn estimators functionally support Array API standard.
+Intel(R) Extension for scikit-Learn preserves input data format for all outputs. For all array inputs except
+SYCL USM arrays `dpnp's <https://github.com/IntelPython/dpnp>`__ ndarray and
+`Data Parallel Control usm_ndarray <https://intelpython.github.io/dpctl/latest/index.html>`__ all computation
+will be only accomplished on CPU unless specified by a `config_context`` with an available GPU device.
+
+Stock scikit-learn uses `config_context(array_api_dispatch=True)` for enabling Array API
+`support <https://scikit-learn.org/1.5/modules/array_api.html>`__.
+If `array_api_dispatch` is enabled and the installed Scikit-Learn version supports array API, then the original
+inputs are used when falling back to Scikit-Learn functionality.
+
+.. note::
+    Data Parallel Control usm_ndarray or DPNP ndarray inputs will use host numpy data copies when
+    falling back to Scikit-Learn since they are not array API compliant.
+.. note::
+    Functional support doesn't guarantee that after the model is trained, fitted attributes that are arrays
+    will also be from the same namespace as the training data.
+
+
+Example usage
+=============
+
+DPNP ndarrays
+-------------
+
+Here is an example code to demonstrate how to use `dpnp <https://github.com/IntelPython/dpnp>`__ arrays to
+run `RandomForestRegressor` on a GPU without `config_context(array_api_dispatch=True)`:
+
+.. literalinclude:: ../../examples/sklearnex/random_forest_regressor_dpnp.py
+	   :language: python
+
+
+.. note::
+    Functional support doesn't guarantee that after the model is trained, fitted attributes that are arrays
+    will also be from the same namespace as the training data.
+
+For example, if `dpnp's <https://github.com/IntelPython/dpnp>`__ namespace was used for training,
+then fitted attributes will be on the CPU and `numpy.ndarray` data format.
+
+DPCTL usm_ndarrays
+------------------
+Here is an example code to demonstrate how to use `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__
+arrays to run `RandomForestClassifier` on a GPU witout `config_context(array_api_dispatch=True)`:
+
+.. literalinclude:: ../../examples/sklearnex/random_forest_classifier_dpctl.py
+	   :language: python
+
+As on previous example, if `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__ Array API namespace was
+used for training, then fitted attributes will be on the CPU and `numpy.ndarray` data format.
+
+Use of `array-api-strict`
+-------------------------
+
+Here is an example code to demonstrate how to use `array-api-strict <https://github.com/data-apis/array-api-strict>`__
+arrays to run `DBSCAN`.
+
+.. literalinclude:: ../../examples/sklearnex/dbscan_array_api.py
+	   :language: python
diff --git a/2025.1/_sources/blogs.rst.txt b/2025.1/_sources/blogs.rst.txt
new file mode 100644
index 0000000000..2d9b0024e5
--- /dev/null
+++ b/2025.1/_sources/blogs.rst.txt
@@ -0,0 +1,35 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _blogs:
+
+Follow us on Medium
+--------------------
+We publish blogs on Medium, so `follow us <https://medium.com/intel-analytics-software/tagged/machine-learning>`_
+to learn tips and tricks for more efficient data analysis the help of |intelex|.
+Here are our latest blogs:
+
+- `Save Time and Money with Intel Extension for Scikit-learn <https://medium.com/intel-analytics-software/save-time-and-money-with-intel-extension-for-scikit-learn-33627425ae4>`_,
+- `Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors <https://medium.com/intel-analytics-software/superior-machine-learning-performance-on-the-latest-intel-xeon-scalable-processor-efdec279f5a3>`_,
+- `Leverage Intel Optimizations in Scikit-Learn <https://medium.com/intel-analytics-software/leverage-intel-optimizations-in-scikit-learn-f562cb9d5544>`_,
+- `Intel Gives Scikit-Learn the Performance Boost Data Scientists Need <https://medium.com/intel-analytics-software/intel-gives-scikit-learn-the-performance-boost-data-scientists-need-42eb47c80b18>`_,
+- `From Hours to Minutes: 600x Faster SVM <https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae>`_,
+- `Improve the Performance of XGBoost and LightGBM Inference <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_,
+- `Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit <https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a>`_,
+- `Accelerate Your scikit-learn Applications <https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e>`_,
+- `Accelerate Linear Models for Machine Learning <https://medium.com/intel-analytics-software/accelerating-linear-models-for-machine-learning-5a75ff50a0fe>`_,
+- `Accelerate K-Means Clustering <https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1>`_.
+- `Why Pay More for Machine Learning? <https://medium.com/intel-analytics-software/why-pay-more-for-machine-learning-893683bd78e4>`_.
diff --git a/2025.1/_sources/contribute.rst.txt b/2025.1/_sources/contribute.rst.txt
new file mode 100644
index 0000000000..28283bd9d8
--- /dev/null
+++ b/2025.1/_sources/contribute.rst.txt
@@ -0,0 +1,64 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+##################
+How to Contribute
+##################
+
+As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn. 
+This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.
+
+Licensing
+----------------------------------
+Intel(R) Extension for Scikit-learn uses the `Apache 2.0 License <https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE>`_. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.
+
+Pull Requests
+----------------------------------
+No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.
+
+Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:
+
+::
+
+  git config --global user.name "Kate Developer"
+  git config --global user.email kate.developer@company.com
+
+Before Contributing Changes
+********************************************
+
+Make sure you can build the product and run all the tests with your patch.
+For a larger feature, provide a relevant test.
+Document your code. Our project uses reStructuredText for documentation.
+For new file(s), specify the appropriate copyright year in the first line.
+Submit a pull request into the master branch.
+Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.
+
+Code Style
+-------------------
+
+We use `black <https://black.readthedocs.io/en/stable/>`_ and `isort <https://pycqa.github.io/isort/>`_ formatters for Python* code. 
+The line length is 90 characters; use default options otherwise. You can find the linter configuration in `.pyproject.toml <https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml>`_.
+
+A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.
+
+Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:
+
+::
+
+  pip install pre-commit
+  pre-commit install
+
+
diff --git a/2025.1/_sources/deprecation.rst.txt b/2025.1/_sources/deprecation.rst.txt
new file mode 100644
index 0000000000..5bc3f3fd19
--- /dev/null
+++ b/2025.1/_sources/deprecation.rst.txt
@@ -0,0 +1,30 @@
+.. ******************************************************************************
+.. * Copyright 2023 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+
+Deprecation Notice
+==================
+
+This page provides information about the deprecations of a specific |intelex| functionality. 
+
+macOS* Support
+**************
+
+**Deprecation:** macOS* support is deprecated for |intelex|. The 2023.x releases are the last to provide it. 
+
+**Reasons for deprecation:** No modern X86 macOS*-based systems are to be released.
+
+**Alternatives:** The 2023.x version on macOS*. 
diff --git a/2025.1/_sources/distributed-mode.rst.txt b/2025.1/_sources/distributed-mode.rst.txt
new file mode 100644
index 0000000000..cfeee2c611
--- /dev/null
+++ b/2025.1/_sources/distributed-mode.rst.txt
@@ -0,0 +1,52 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _distributed:
+
+Distributed Mode
+================
+
+|intelex| offers Single Program, Multiple Data (SPMD) supported interfaces for distributed computing.
+Several `GPU-supported algorithms <https://intel.github.io/scikit-learn-intelex/latest/oneapi-gpu.html#>`_ 
+also provide distributed, multi-GPU computing capabilities via integration with ``mpi4py``. The prerequisites 
+match those of GPU computing, along with an MPI backend of your choice (`Intel MPI recommended 
+<https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html#gs.dcan6r>`_, available 
+via ``impi-devel`` python package) and the ``mpi4py`` python package. If using |intelex| 
+`installed from sources <https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md#build-from-sources>`_,
+ensure that the spmd_backend is built.
+
+Note that |intelex| now supports GPU offloading to speed up MPI operations. This is supported automatically with 
+some MPI backends, but in order to use GPU offloading with Intel MPI, set the following environment variable (providing
+data on device without this may lead to a runtime error):
+ 
+::
+
+     export I_MPI_OFFLOAD=1
+
+Estimators can be imported from the ``sklearnex.spmd`` module. Data should be distributed across multiple nodes as 
+desired, and should be transfered to a dpctl or dpnp array before being passed to the estimator. View a full 
+example of this process in the |intelex| repository, where many examples of our SPMD-supported estimators are 
+available: https://github.com/intel/scikit-learn-intelex/blob/main/examples/sklearnex/. To run:
+
+::
+
+  mpirun -n 4 python linear_regression_spmd.py
+
+Note that additional mpirun arguments can be added as desired. SPMD-supported estimators are listed in the 
+`algorithms support documentation <https://intel.github.io/scikit-learn-intelex/latest/algorithms.html#spmd-support>`_.
+
+Additionally, daal4py offers some distributed functionality, see 
+`documentation <https://intelpython.github.io/daal4py/scaling.html>`_ for further details.
diff --git a/2025.1/_sources/guide/acceleration.rst.txt b/2025.1/_sources/guide/acceleration.rst.txt
new file mode 100644
index 0000000000..952ee73539
--- /dev/null
+++ b/2025.1/_sources/guide/acceleration.rst.txt
@@ -0,0 +1,86 @@
+.. ******************************************************************************
+.. * Copyright 2022 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+########################
+Tuning Guide
+########################
+
+The performance of some algorithms changes based on the parameters that are used.
+This section denotes the details of such cases.
+
+Refer to :ref:`sklearn_algorithms` to see the full list of algorithms, parameters, and data formats supported in |intelex|.
+
+.. _acceleration_tsne:
+
+TSNE
+----
+
+TSNE algorithm consists of two components: KNN and Gradient Descent.
+The overall acceleration of TSNE depends on the acceleration of each of these algorithms.
+
+- The KNN part of the algorithm supports all parameters except:
+
+  - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+- The Gradient Descent part of the algorithm supports all parameters except:
+
+  - ``n_components`` = `3`
+  - ``method`` = `'exact'`
+  - ``verbose`` != `0`
+
+To get better performance, use parameters supported by both components.
+
+.. _acceleration_rf:
+
+Random Forest
+-------------
+
+Random Forest models accelerated with |intelex| and using the `hist` splitting
+method discretize training data by creating a histogram with a configurable
+number of bins. The following keyword arguments can be used to influence the
+created histogram.
+
+.. list-table::
+   :widths: 10 10 10 30
+   :header-rows: 1
+   :align: left
+
+   * - Keyword argument
+     - Possible values
+     - Default value
+     - Description
+   * - ``maxBins``
+     - `[0, inf)`
+     - ``256``
+     - Number of bins in the histogram with the discretized training data. The
+       value ``0`` disables data discretization.
+   * - ``minBinSize``
+     - `[1, inf)`
+     - ``5``
+     - Minimum number of training data points in each bin after discretization.
+   * - ``binningStrategy``
+     - ``quantiles, averages``
+     - ``quantiles``
+     - Selects the algorithm used to calculate bin edges. ``quantiles``
+       results in bins with a similar amount of training data points. ``averages``
+       divides the range of values observed in the training data set into
+       equal-width bins of size `(max - min) / maxBins`.
+
+Note that using discretized training data can greatly accelerate model training
+times, especially for larger data sets. However, due to the reduced fidelity of
+the data, the resulting model can present worse performance metrics compared to
+a model trained on the original data. In such cases, the number of bins can be
+increased with the ``maxBins`` parameter, or binning can be disabled entirely by
+setting ``maxBins=0``.
diff --git a/2025.1/_sources/index.rst.txt b/2025.1/_sources/index.rst.txt
new file mode 100644
index 0000000000..4663cd1d5b
--- /dev/null
+++ b/2025.1/_sources/index.rst.txt
@@ -0,0 +1,138 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |intelex_repo| replace:: |intelex| repository
+.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
+
+.. _index:
+
+#########
+|intelex|
+#########
+
+Intel(R) Extension for Scikit-learn is a **free software AI accelerator** designed to deliver up to **100X** faster performance for your existing scikit-learn code.
+The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.
+
+.. rubric:: Designed for Data Scientists and Framework Designers
+
+
+Use Intel(R) Extension for Scikit-learn, to:
+
+* Speed up training and inference by up to 100x with the equivalent mathematical accuracy
+* Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs
+* Integrate the extension into your existing Scikit-learn applications without code modifications
+* Enable and disable the extension with a couple of lines of code or at the command line
+
+Intel(R) Extension for Scikit-learn is also a part of `Intel(R) AI Tools <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html>`_.
+
+
+.. image:: _static/scikit-learn-acceleration.PNG
+  :width: 800
+
+
+These performance charts use benchmarks that you can find in the `scikit-learn bench repository <https://github.com/IntelPython/scikit-learn_bench>`_. 
+
+
+Supported Algorithms 
+---------------------
+
+See all of the :ref:`sklearn_algorithms`. 
+
+
+Intel(R) Optimizations
+----------------------------------
+
+Enable Intel(R) CPU Optimizations
+*********************************
+
+::
+
+   import numpy as np
+   from sklearnex import patch_sklearn
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+Enable Intel(R) GPU optimizations
+*********************************
+::
+
+   import numpy as np
+   import dpctl
+   from sklearnex import patch_sklearn, config_context
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   with config_context(target_offload="gpu:0"):
+       clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+
+
+.. toctree::
+   :caption: Get Started
+   :hidden:
+   :maxdepth: 3
+
+   quick-start.rst
+   samples.rst
+   kaggle.rst
+   
+.. toctree::
+   :caption: Developer Guide
+   :hidden:
+   :maxdepth: 2
+
+   algorithms.rst
+   oneAPI and GPU support <oneapi-gpu.rst>
+   distributed-mode.rst
+   non-scikit-algorithms.rst
+   input-types.rst
+   array_api.rst
+   verbose.rst
+   preview.rst
+   deprecation.rst
+   
+
+.. toctree::
+   :caption: Performance
+   :hidden:
+   :maxdepth: 2
+
+   guide/acceleration.rst
+
+.. toctree::
+   :caption: Learn 
+   :hidden:
+   :maxdepth: 2
+
+   Tutorials & Case Studies <tutorials.rst>
+   Medium Blogs <blogs.rst>
+
+.. toctree::
+   :caption: More
+   :hidden:
+   :maxdepth: 2
+   
+   Support <support.rst>
+   contribute.rst
+   license.rst
diff --git a/2025.1/_sources/input-types.rst.txt b/2025.1/_sources/input-types.rst.txt
new file mode 100644
index 0000000000..cd1714feb8
--- /dev/null
+++ b/2025.1/_sources/input-types.rst.txt
@@ -0,0 +1,43 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _input_types:
+
+=====================
+Supported input types
+=====================
+
+Just like Scikit-Learn estimators, estimators from Intel(R)'s Extension are able to accept
+and work with different classes of input data, including:
+
+- `Numpy <https://numpy.org/>`__ arrays.
+- Other array classes implementing the `Array API <https://data-apis.org/array-api/latest/>`__ protocol
+  (see :ref:`array_api` for details).
+- `SciPy sparse arrays and sparse matrices <https://docs.scipy.org/doc/scipy/tutorial/sparse.html>`__ (depending on the estimator).
+- `Pandas <https://pandas.pydata.org>`__ ``DataFrame`` and ``Series`` classes.
+
+In addition, Intel(R)'s Extension also supports:
+
+- `dpnp <https://github.com/IntelPython/dpnp>`__ arrays.
+- `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__ arrays.
+
+Stock Scikit-Learn estimators, depending on the version, might offer support for additional
+input types beyond this list, such as ``DataFrame`` and ``Series`` classes from other libraries
+like `Polars <https://pola.rs>`__.
+
+Intel(R)'s Extension currently does not offer accelerated routines for input types not listed
+here - when receiving an unsupported class, estimators will fall back to stock Scikit-Learn to
+handle it, so make sure to convert them to a supported type when using Intel(R)'s Extension.
diff --git a/2025.1/_sources/kaggle.rst.txt b/2025.1/_sources/kaggle.rst.txt
new file mode 100644
index 0000000000..364ab723d1
--- /dev/null
+++ b/2025.1/_sources/kaggle.rst.txt
@@ -0,0 +1,49 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _kaggle:
+
+####################
+Kaggle Kernels
+####################
+
+See Kaggle notebooks created by |intelex| developers.
+
+Acceleration
+---------------------------------
+
+`Introduction to scikit-learn-intelex <https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook>`_
+provides a summary of the speedup you can achieve with |intelex|.
+
+Machine Learning Workflows
+---------------------------------
+
+Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.
+
+Kaggle kernels that use scikit-learn and |intelex|:
+******************************************************************
+
+.. toctree::
+
+   Classification Tasks <kaggle/classification.rst>
+   Regression Tasks <kaggle/regression.rst>
+
+Kaggle kernels that use AutoML with |intelex|:
+******************************************************************
+
+.. toctree::
+
+   AutoML Workflows <kaggle/automl.rst>
diff --git a/2025.1/_sources/kaggle/automl.rst.txt b/2025.1/_sources/kaggle/automl.rst.txt
new file mode 100644
index 0000000000..0156e5ec89
--- /dev/null
+++ b/2025.1/_sources/kaggle/automl.rst.txt
@@ -0,0 +1,66 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |automl_with_intelex_jun| replace:: AutoML MultiClass Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with |intelex| 
+.. _automl_with_intelex_jun: https://www.kaggle.com/alex97andreev/tps-jun-autogluon-with-sklearnex
+
+.. |automl_with_intelex_tps_oct| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest) using AutoGluon with |intelex|
+.. _automl_with_intelex_tps_oct: https://www.kaggle.com/lordozvlad/fast-automl-with-intel-extension-for-scikit-learn/notebook
+
+.. |automl_with_intelex_tps_nov| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using EvalML and AutoGluon with |intelex|
+.. _automl_with_intelex_tps_nov: https://www.kaggle.com/lordozvlad/tps-nov-automl-with-intel-extension
+
+.. |automl_with_intelex_titanic| replace:: AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with |intelex|
+.. _automl_with_intelex_titanic: https://www.kaggle.com/lordozvlad/titanic-automl-with-intel-extension-for-sklearn/notebook
+
+.. |automl_with_intelex_tps_jan| replace:: AutoML Binary Classification (Random Forest, SVR, Blending) using PyCaret with |intelex|
+.. _automl_with_intelex_tps_jan: https://www.kaggle.com/code/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex/notebook
+
+
+Kaggle Kernels that use AutoML and |intelex|
+--------------------------------------------
+
+The following Kaggle kernels show how to patch autoML frameworks with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 40 20
+
+   * - Kernel
+     - Goal
+   * - |automl_with_intelex_jun|_
+
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+   * - |automl_with_intelex_titanic|_
+
+       **Data:** Titanic datset
+     - Predict whether a passenger survivies
+   * - |automl_with_intelex_tps_oct|_
+
+       **Data:** [TPS Oct 2021] Synthetic molecular response data
+     - Predict the biological response of molecules given various chemical properties
+   * - |automl_with_intelex_tps_nov|_
+
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+     - Identify spam emails via features extracted from the email
+   * - |automl_with_intelex_tps_jan|_
+
+       **Data:** [TPS Jan 2022] Fictional Sales data
+     - Predict the corresponding item sales for each date-country-store-item combination
\ No newline at end of file
diff --git a/2025.1/_sources/kaggle/classification.rst.txt b/2025.1/_sources/kaggle/classification.rst.txt
new file mode 100644
index 0000000000..c0176cab62
--- /dev/null
+++ b/2025.1/_sources/kaggle/classification.rst.txt
@@ -0,0 +1,244 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Kaggle Kernels for Classification Tasks
+***************************************
+
+The following Kaggle kernels show how to patch scikit-learn with |intelex| for various classification tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+Binary Classification
++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Logistic Regression for Binary Classification
+       <https://www.kaggle.com/alexeykolobyanin/tps-nov-log-regression-with-sklearnex-17x-speedup>`_
+     
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+
+     - Identify spam emails via features extracted from the email
+     - 
+
+       - data preprocessing (normalization)
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Feature Importance in Random Forest for Binary Classification
+       <https://www.kaggle.com/code/lordozvlad/fast-feature-importance-using-scikit-learn-intelex/notebook>`_
+     
+       **Data:** [TPS Nov 2021] Synthetic spam emails data
+
+     - Identify spam emails via features extracted from the email
+     - 
+      
+       - reducing DataFrame memory usage
+       - computing feature importance with ELI5 and the default scikit-learn permutation importance
+       - training using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest for Binary Classification
+       <https://www.kaggle.com/andreyrus/tps-apr-rf-with-intel-extension-for-scikit-learn>`_
+     
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+      
+       - data preprocessing
+       - feature construction
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for Binary Classification
+       <https://www.kaggle.com/napetrov/tps04-svm-with-intel-extension-for-scikit-learn>`_
+       
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+     
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) with Feature Preprocessing for Binary Classification
+       <https://www.kaggle.com/napetrov/tps04-svm-with-scikit-learn-intelex>`_
+      
+       **Data:** [TPS Apr 2021] Synthetic data based on Titanic dataset
+     - Predict whether a passenger survivies
+     - 
+     
+       - data preprocessing
+       - feature engineering
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+MultiClass Classification
++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Logistic Regression for MultiClass Classification with Quantile Transformer
+       <https://www.kaggle.com/kppetrov/tps-jun-fast-logreg-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+
+       - data preprocessing with Quantile Transformer
+       - training and prediction using scikit-learn-intelex
+       - search for optimal paramters using Optuna
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for MultiClass Classification (example 1)
+       <https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS May 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+
+   * - `Stacking Classifer with Logistic Regression, kNN, Random Forest, and Quantile Transformer
+       <https://www.kaggle.com/owerbat/tps-jun-fast-stacking-with-scikit-learn-intelex>`_
+      
+       **Data:** [TPS Jun 2021] Synthetic eCommerce data
+     - Predict the category of an eCommerce product
+     - 
+
+       - data preprocessing: one-hot encoding, dimensionality reduction with PCA, normalization
+       - creating a stacking classifier with logistic regression, kNN, and random forest,
+         and a pipeline of Quantile Transformer and another logistic regression as a final estimator
+       - searching for optimal parameters for the stacking classifier
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for MultiClass Classification (example 2)
+       <https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup>`_
+       
+       **Data:** [TPS Dec 2021] Synthetic Forest Cover Type data
+     - Predict the forest cover type
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Feature Importance in Random Forest for MultiClass Classification
+       <https://www.kaggle.com/code/lordozvlad/tps-dec-fast-feature-importance-with-sklearnex>`_
+     
+       **Data:** [TPS Dec 2021] Synthetic Forest Cover Type data
+
+     - Predict the forest cover type
+     - 
+      
+       - reducing DataFrame memory usage
+       - computing feature importance with ELI5
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `k-Nearest Neighbors (kNN) for MultiClass Classification
+       <https://www.kaggle.com/code/alexeykolobyanin/tps-feb-knn-with-sklearnex-13x-speedup>`_
+       
+       **Data:** [TPS Feb 2022] Bacteria DNA
+     - Predict bacteria species based on repeated lossy measurements of DNA snippets
+     - 
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Classification Tasks in Computer Vision
++++++++++++++++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Support Vector Classification (SVC) for MultiClass Classification (CV task)
+       <https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-mnist?scriptVersionId=58739300>`_
+       
+       **Data:** Digit Recognizer (MNIST)
+     - Recognize hand-written digits
+     - 
+       
+       - data preprocessing
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `k-Nearest Neighbors (kNN) for MultiClass Classification (CV task)
+       <https://www.kaggle.com/kppetrov/fast-knn-using-scikit-learn-intelex-for-mnist?scriptVersionId=58738635>`_
+       
+       **Data:** Digit Recognizer (MNIST)
+     - Recognize hand-written digits
+     - 
+     
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Classification Tasks in Natural Language Processing
++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Support Vector Classification (SVC) for a Binary Classification (NLP task)
+       <https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-nlp?scriptVersionId=58739339>`_
+       
+       **Data:** Natural Language Processing with Disaster Tweets
+     - Predict which tweets are about real disasters and which ones are not
+     - 
+     
+       - data preprocessing
+       - TF-IDF calculation
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `One-vs-Rest Support Vector Machine (SVM) with Text Data for MultiClass Classification
+       <https://www.kaggle.com/kppetrov/using-scikit-learn-intelex-for-what-s-cooking?scriptVersionId=58739642>`_
+      
+       **Data:** What's Cooking
+     - Use recipe ingredients to predict the cuisine
+     - 
+     
+       - feature extraction using TfidfVectorizer
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Support Vector Classification (SVC) for Binary Classification with Sparse Data (NLP task)
+       <https://www.kaggle.com/code/alex97andreev/fast-svm-for-sparse-data-from-nlp-problem>`_
+       
+       **Data:** Stack Overflow questions
+     - Predict the binary quality rating for Stack Overflow questions
+     - 
+     
+       - data preprocessing
+       - TF-IDF calculation
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
diff --git a/2025.1/_sources/kaggle/regression.rst.txt b/2025.1/_sources/kaggle/regression.rst.txt
new file mode 100644
index 0000000000..abdbebc15b
--- /dev/null
+++ b/2025.1/_sources/kaggle/regression.rst.txt
@@ -0,0 +1,156 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Kaggle Kernels for Regression Tasks
+************************************
+
+The following Kaggle kernels show how to patch scikit-learn with |intelex| for various regression tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with |intelex|.
+
+.. include:: /kaggle/note-about-tps.rst
+
+Using a Single Regressor
+++++++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel
+       <https://www.kaggle.com/kppetrov/tps-jul-2021-baseline-with-nusvr-model/notebook>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - data preprocessing
+       - search for optimal paramters using Optuna
+       - training and prediction using scikit-learn-intelex
+   * - `Nu Support Vector Regression (nuSVR)
+       <https://www.kaggle.com/alexeykolobyanin/tps-aug-nusvr-with-intel-extension-for-sklearn>`__
+     
+       **Data:** [TPS Aug 2021] Synthetic loan data
+     - Calculate loss associated with a loan defaults
+     -
+
+       - data preprocessing
+       - feature engineering
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Nu Support Vector Regression (nuSVR)
+       <https://www.kaggle.com/alexeykolobyanin/house-prices-nusvr-sklearn-intelex-4x-speedup>`__
+       
+       **Data:** House Prices dataset
+     - Predict sale prices for a property based on its characteristics
+     -
+
+       - data preprocessing
+       - exploring outliers
+       - feature engineering
+       - filling missing values
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest Regression
+       <https://www.kaggle.com/pahandrovich/tps-jul-2021-fast-randomforest-with-sklearnex>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - checking correlation between features
+       - search for best paramters using GridSearchCV
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Random Forest Regression with Feature Engineering
+       <https://www.kaggle.com/alexeykolobyanin/tps-jul-rf-with-intel-extension-for-scikit-learn>`_
+     
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - data preprocessing
+       - feature engineering
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Random Forest Regression with Feature Importance Computation
+       <https://www.kaggle.com/code/lordozvlad/tps-mar-fast-workflow-using-scikit-learn-intelex>`_
+
+       **Data:** [TPS Mar 2022] Spatio-temporal traffic data
+     - Forecast twelve-hours of traffic flow in a major U.S. metropolitan area
+     -
+
+       - feature engineering
+       - computing feature importance with ELI5
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+   * - `Ridge Regression
+       <https://www.kaggle.com/alexeykolobyanin/tps-sep-ridge-with-sklearn-intelex-2x-speedup>`_
+     
+     
+       **Data:** [TPS Sep 2021] Synthetic insurance data
+     - Predict the probability of a customer making a claim upon an insurance policy
+     -
+
+       - data preprocessing
+       - filling missing values
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+Stacking Regressors
++++++++++++++++++++
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+   :widths: 30 20 30
+
+   * - Kernel
+     - Goal
+     - Content
+   * - `Stacking Regressor with Random Fores, SVR, and LASSO
+       <https://www.kaggle.com/alexeykolobyanin/tps-jul-stacking-with-scikit-learn-intelex>`_
+       
+       **Data:** [TPS Jul 2021] Synthetic pollution data
+     - Predict air pollution measurements over time based on weather and input values from multiple sensors
+     - 
+
+       - feature engineering
+       - creating a stacking regressor
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
+
+   * - `Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data
+       <https://www.kaggle.com/alexeykolobyanin/predict-sales-stacking-with-scikit-learn-intelex>`_
+       
+       **Data:** Predict Future Sales dataset
+     - Predict total sales for every product and store in the next month based on daily sales data
+     -
+
+       - data preprocessing
+       - creating a stacking regressor
+       - search for optimal parameters using Optuna
+       - training and prediction using scikit-learn-intelex
+       - performance comparison to scikit-learn
diff --git a/2025.1/_sources/license.rst.txt b/2025.1/_sources/license.rst.txt
new file mode 100644
index 0000000000..df68abaea6
--- /dev/null
+++ b/2025.1/_sources/license.rst.txt
@@ -0,0 +1,23 @@
+.. ******************************************************************************
+.. * Copyright 2023 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _license:
+
+############################################
+License
+############################################
+
+License: https://github.com/intel/scikit-learn-intelex/blob/main/LICENSE
\ No newline at end of file
diff --git a/2025.1/_sources/non-scikit-algorithms.rst.txt b/2025.1/_sources/non-scikit-algorithms.rst.txt
new file mode 100644
index 0000000000..620461843f
--- /dev/null
+++ b/2025.1/_sources/non-scikit-algorithms.rst.txt
@@ -0,0 +1,44 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Non-Scikit-Learn Algorithms
+===========================
+Algorithms not presented in the original scikit-learn are described here. All algorithms are 
+available for both CPU and GPU (including distributed mode)
+
+BasicStatistics
+---------------
+.. autoclass:: sklearnex.basic_statistics.BasicStatistics
+.. automethod:: sklearnex.basic_statistics.BasicStatistics.fit
+
+IncrementalBasicStatistics
+--------------------------
+.. autoclass:: sklearnex.basic_statistics.IncrementalBasicStatistics
+.. automethod:: sklearnex.basic_statistics.IncrementalBasicStatistics.fit
+.. automethod:: sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit
+
+IncrementalEmpiricalCovariance
+------------------------------
+.. autoclass:: sklearnex.covariance.IncrementalEmpiricalCovariance
+.. automethod:: sklearnex.covariance.IncrementalEmpiricalCovariance.fit
+.. automethod:: sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit
+
+IncrementalLinearRegression
+---------------------------
+.. autoclass:: sklearnex.linear_model.IncrementalLinearRegression
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.fit
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.partial_fit
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.predict
\ No newline at end of file
diff --git a/2025.1/_sources/oneapi-gpu.rst.txt b/2025.1/_sources/oneapi-gpu.rst.txt
new file mode 100644
index 0000000000..37bfbf3a92
--- /dev/null
+++ b/2025.1/_sources/oneapi-gpu.rst.txt
@@ -0,0 +1,114 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _oneapi_gpu:
+
+##############################################################
+oneAPI and GPU support in |intelex|
+##############################################################
+
+|intelex| supports oneAPI concepts, which
+means that algorithms can be executed on different devices: CPUs and GPUs.
+This is done via integration with
+`dpctl <https://intelpython.github.io/dpctl/latest/index.html>`_ package that
+implements core oneAPI concepts like queues and devices.
+
+Prerequisites
+-------------
+
+For execution on GPU, DPC++ compiler runtime and driver are required. Refer to `DPC++ system
+requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_ for details.
+
+DPC++ compiler runtime can be installed either from PyPI or Anaconda:
+
+- Install from PyPI::
+
+     pip install dpcpp-cpp-rt
+
+- Install using Conda via the Intel repository::
+
+     conda install dpcpp_cpp_rt -c https://software.repos.intel.com/python/conda/
+
+Device offloading
+-----------------
+
+|intelex| offers two options for running an algorithm on a
+specific device with the help of dpctl:
+
+- Pass input data as `dpctl.tensor.usm_ndarray <https://intelpython.github.io/dpctl/latest/docfiles/dpctl/usm_ndarray.html#dpctl.tensor.usm_ndarray>`_ to the algorithm.
+
+  The computation will run on the device where the input data is
+  located, and the result will be returned as :code:`usm_ndarray` to the same
+  device.
+
+  .. note::
+    All the input data for an algorithm must reside on the same device.
+
+  .. warning::
+    The :code:`usm_ndarray` can only be consumed by the base methods
+    like :code:`fit`, :code:`predict`, and :code:`transform`.
+    Note that only the algorithms in |intelex| support
+    :code:`usm_ndarray`. The algorithms from the stock version of scikit-learn
+    do not support this feature.
+- Use global configurations of |intelex|\*:
+  
+  1. The :code:`target_offload` option can be used to set the device primarily
+     used to perform computations. Accepted data types are :code:`str` and
+     :code:`dpctl.SyclQueue`. If you pass a string to :code:`target_offload`,
+     it should either be ``"auto"``, which means that the execution
+     context is deduced from the location of input data, or a string
+     with SYCL* filter selector. The default value is ``"auto"``.
+  
+  2. The :code:`allow_fallback_to_host` option
+     is a Boolean flag. If set to :code:`True`, the computation is allowed 
+     to fallback to the host device when a particular estimator does not support
+     the selected device. The default value is :code:`False`.
+
+These options can be set using :code:`sklearnex.set_config()` function or
+:code:`sklearnex.config_context`. To obtain the current values of these options,
+call :code:`sklearnex.get_config()`.
+
+.. note::
+     Functions :code:`set_config`, :code:`get_config` and :code:`config_context`
+     are always patched after the :code:`sklearnex.patch_sklearn()` call.
+
+.. rubric:: Compatibility considerations
+
+For compatibility reasons, algorithms in |intelex| may be offloaded to the device using
+:code:`daal4py.oneapi.sycl_context`. However, it is recommended to use one of the options
+described above for device offloading instead of using :code:`sycl_context`.
+
+Example
+-------
+
+An example on how to patch your code with Intel CPU/GPU optimizations:
+
+.. code-block:: python
+
+   from sklearnex import patch_sklearn, config_context
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   with config_context(target_offload="gpu:0"):
+      clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+
+.. note:: Current offloading behavior restricts fitting and inference of any models to be
+     in the same context or absence of context. For example, a model trained in the GPU context with
+     target_offload="gpu:0" throws an error if the inference is made outside the same GPU context.
diff --git a/2025.1/_sources/preview.rst.txt b/2025.1/_sources/preview.rst.txt
new file mode 100644
index 0000000000..7f19c6a264
--- /dev/null
+++ b/2025.1/_sources/preview.rst.txt
@@ -0,0 +1,73 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _preview:
+
+#####################
+Preview Functionality
+#####################
+
+Some of implemented functionality in |intelex| doesn't meet one or few of next requirements
+for being enabled by default for all users:
+
+* The functionality API is not stable and can be changed in future
+* The functionality doesn't have full compatibility with stock Scikit-learn
+* The functionality misses performance targets compared to stock Scikit-learn or previously available version of functionality
+* The functionality is not fully tested
+
+This type of functionality is available under **preview mode** of |intelex| and located in
+the corresponding module (`sklearnex.preview`).
+
+Preview functionality *may* or *may not* participate in patching of Scikit-learn.
+For example, a preview estimator may be a replacement for a stock one or a completely new one.
+
+To enable preview functionality, you need to set the `SKLEARNEX_PREVIEW` environment variable
+to non-empty value before patching of Scikit-learn.
+For example, you can set the environment variable in the following way:
+
+- On Linux* OS ::
+
+     export SKLEARNEX_PREVIEW=1
+
+- On Windows* OS ::
+
+     set SKLEARNEX_PREVIEW=1
+
+Then, you can import Scikit-learn estimator patched with a preview one from `sklearnex.preview` module::
+
+     from sklearnex import patch_sklearn
+     patch_sklearn()
+     from sklearn.decomposition import IncrementalPCA
+     print(IncrementalPCA.__module__)
+     # output:
+     # sklearnex.preview.decomposition.incremental_pca
+
+Current list of preview estimators:
+
+.. list-table::
+   :widths: 30 20 10
+   :header-rows: 1
+   :align: left
+
+   * - Estimator name
+     - Module
+     - Is patching supported
+   * - EmpiricalCovariance
+     - sklearnex.preview.covariance
+     - Yes
+   * - IncrementalPCA
+     - sklearnex.preview.decomposition
+     - Yes
diff --git a/2025.1/_sources/quick-start.rst.txt b/2025.1/_sources/quick-start.rst.txt
new file mode 100644
index 0000000000..bc6ac8798a
--- /dev/null
+++ b/2025.1/_sources/quick-start.rst.txt
@@ -0,0 +1,405 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. |intelex_repo| replace:: |intelex| repository
+.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
+
+####################
+Quick Start
+####################
+
+Get ready to elevate your scikit-learn code with |intelex| and experience the benefits of accelerated performance in just a few simple steps. 
+
+Compatibility with Scikit-learn*
+---------------------------------
+
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.
+
+Integrate |intelex|
+--------------------
+
+Patching 
+**********************
+
+Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension. 
+This action is called ``patching``. This is not a permanent change so you can always undo the patching if necessary.
+
+To patch Intel® Extension for Scikit-learn, use one of these methods: 
+
+.. list-table:: 
+   :header-rows: 1
+   :align: left
+
+   * - Method
+     - Action
+   * - Use a flag in the command line
+     - Run this command:
+
+       :: 
+         
+          python -m sklearnex my_application.py
+   * - Modify your script 
+     - Add the following lines:
+
+       ::
+ 
+          from sklearnex import patch_sklearn
+          patch_sklearn()   
+   * - Import an estimator from the ``sklearnex`` module
+     - Run this command:
+
+       ::
+
+          from sklearnex.neighbors import NearestNeighbors
+
+
+
+These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.
+
+   
+**Example**
+
+This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.
+  
+.. code-block:: python
+  :caption: Example: Drop-In Patching
+   
+    import numpy as np
+    from sklearnex import patch_sklearn
+    patch_sklearn()
+
+    # You need to re-import scikit-learn algorithms after the patch
+    from sklearn.cluster import KMeans
+  
+    # The use of the original Scikit-learn is not changed
+    X = np.array([[1,  2], [1,  4], [1,  0],
+                [10, 2], [10, 4], [10, 0]])
+    kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
+    print(f"kmeans.labels_ = {kmeans.labels_}")
+
+
+Global Patching
+**********************
+
+You can also use global patching to patch all your scikit-learn applications without any additional actions.
+
+Before you begin, make sure that you have read and write permissions for Scikit-learn files. 
+
+With global patching, you can:
+
+.. list-table:: 
+   :header-rows: 1
+   :align: left
+
+   * - Task
+     - Action
+     - Note
+   * - Patch all supported algorithms
+     - Run this command:
+
+       :: 
+         
+          python -m sklearnex.glob patch_sklearn
+     
+     - If you run the global patching command several times with different parameters, then only the last configuration is applied.
+   * - Patch selected algorithms
+     - Use ``--algorithm`` or ``-a`` keys with a list of algorithms to patch. For example, to patch only ``SVC`` and ``RandomForestClassifier`` estimators, run
+
+       ::
+ 
+           python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier
+  
+     -   
+   * - Enable global patching via code
+     - Use the ``patch_sklearn`` function with the ``global_patch`` argument:
+
+       ::
+
+          from sklearnex import patch_sklearn
+          patch_sklearn(global_patch=True)
+          import sklearn
+      
+     - After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.
+   * - Disable patching notifications
+     - Use ``--no-verbose`` or ``-nv`` keys:
+
+       ::
+
+          python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier -nv
+     -  
+   * - Disable global patching
+     - Run this command:
+
+       ::
+
+          python -m sklearnex.glob unpatch_sklearn
+     -
+   * - Disable global patching via code
+     - Use the ``global_patch`` argument in the ``unpatch_sklearn`` function
+
+       ::
+
+          from sklearnex import unpatch_sklearn
+          unpatch_sklearn(global_patch=True)
+     -
+    
+.. tip:: If you clone an environment with enabled global patching, it will already be applied in the new environment.
+
+Unpatching
+**********************
+
+To undo the patch (also called `unpatching`) is to return scikit-learn to original implementation and
+replace patched algorithms with the stock scikit-learn algorithms.
+
+To unpatch successfully, you must reimport the scikit-learn package::
+
+  sklearnex.unpatch_sklearn()
+  # Re-import scikit-learn algorithms after the unpatch
+  from sklearn.cluster import KMeans  
+
+
+Installation 
+--------------------
+
+.. contents:: :local:
+
+.. tip:: To prevent version conflicts, we recommend creating and activating a new environment for |intelex|. 
+
+Install from PyPI 
+**********************
+
+Recommended by default. 
+
+To install |intelex|, run:
+
+::
+
+  pip install scikit-learn-intelex
+
+**Supported Configurations**
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+
+   * - OS / Python version
+     - Python 3.9
+     - Python 3.10
+     - Python 3.11
+     - Python 3.12
+   * - Linux* OS
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+   * - Windows* OS
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+
+
+
+Install from Anaconda* Cloud
+********************************************
+
+To prevent version conflicts, we recommend installing `scikit-learn-intelex` into a new conda environment.
+
+.. tabs::
+
+   .. tab:: Conda-Forge channel
+
+      Recommended by default. 
+      
+      To install, run::
+
+        conda install scikit-learn-intelex -c conda-forge
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+         * - Windows* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+
+
+   .. tab:: Intel channel
+
+      Recommended for the Intel® Distribution for Python users. 
+
+      To install, run::
+
+        conda install scikit-learn-intelex -c https://software.repos.intel.com/python/conda/
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+         * - Windows* OS
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+ 
+
+
+   .. tab:: Main channel
+
+      To install, run::
+
+        conda install scikit-learn-intelex
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+         * - Windows* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+
+
+
+Build from Sources
+**********************
+
+See `Installation instructions <https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md>`_ to build |intelex| from the sources.
+
+Install Intel*(R) AI Tools
+****************************
+
+Download the Intel AI Tools `here <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html>`_. The extension is already included.
+
+Release Notes
+-------------------
+
+See the `Release Notes <https://github.com/intel/scikit-learn-intelex/releases>`_ for each version of Intel® Extension for Scikit-learn*.  
+
+System Requirements 
+--------------------
+
+Hardware Requirements
+**********************
+
+.. tabs::
+
+   .. tab:: CPU
+
+      All processors with ``x86`` architecture with at least one of the following instruction sets:
+
+        - SSE2
+        - SSE4.2
+        - AVX2
+        - AVX512
+       
+      .. note:: ARM* architecture is not supported.
+
+   .. tab:: GPU
+
+      - All Intel® integrated and discrete GPUs
+      - Intel® GPU drivers
+
+
+.. tip:: Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our :ref:`blogs <blogs>`.
+
+
+Software Requirements
+**********************
+
+.. tabs::
+
+   .. tab:: CPU
+
+      - Linux* OS: Ubuntu* 18.04 or newer
+      - Windows* OS 10 or newer
+      - Windows* Server 2019 or newer
+
+   .. tab:: GPU
+
+      - Linux* OS: Ubuntu* 18.04 or newer
+      - Windows* OS 10 or newer
+      - Windows* Server 2019 or newer
+      
+      .. important::
+         
+         If you use accelerators, refer to `oneAPI DPC++/C++ Compiler System Requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_.
+
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:
+
+* 1.0.X
+* 1.1.X
+* 1.2.X 
+* 1.3.X
+
+Memory Requirements
+**********************
+By default, algorithms in |intelex| run in the multi-thread mode. This mode uses all available threads. 
+Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Single-thread mode
+     - Multi-thread mode
+   * - SVM
+     - Both Scikit-learn and |intelex| consume approximately the same amount of RAM.
+     - In |intelex|, an algorithm with ``N`` threads consumes ``N`` times more RAM.
+
+In all |intelex| algorithms with GPU support, computations run on device memory. 
+The device memory must be large enough to store a copy of the entire dataset.
+You may also require additional device memory for internal arrays that are used in computation.
+
+
+.. seealso::
+
+   :ref:`Samples<samples>`
diff --git a/2025.1/_sources/samples.rst.txt b/2025.1/_sources/samples.rst.txt
new file mode 100644
index 0000000000..4ca25f6d92
--- /dev/null
+++ b/2025.1/_sources/samples.rst.txt
@@ -0,0 +1,75 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _samples:
+
+#######
+Samples 
+#######
+
+The following samples are also provided as Jupyter notebooks in |intelex| repository.
+If you want to run them locally, refer to `these instructions
+<https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks>`_.
+
+.. rubric:: Classification Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   k-Nearest Neighbors (kNN) for MNIST dataset <samples/knn_mnist.ipynb>
+   Logistic Regression for Cifar dataset <samples/logistictic_regression_cifar.ipynb>
+   Support Vector Classification (SVC) for Adult dataset <samples/svc_adult.ipynb>
+
+.. rubric:: Regression Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   ElasticNet for Airlines DepDelay dataset <samples/ElasticNet.ipynb>
+   Lasso Regression for YearPredictionMSD dataset <samples/lasso_regression.ipynb>
+   Linear Regression for YearPredictionMSD dataset <samples/linear_regression.ipynb>
+   Nu-Support Vector Regression (NuSVR) for Medical Charges dataset <samples/nusvr_medical_charges.ipynb>
+   Random Forest for Yolanda dataset <samples/random_forest_yolanda.ipynb>
+   Rigde Regression for Airlines DepDelay dataset <samples/ridge_regression.ipynb>
+
+.. rubric:: Clustering Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   Kmeans for spoken arabic digit dataset <samples/kmeans.ipynb>
+   DBSCAN for spoken arabic digit dataset <samples/dbscan.ipynb>
+
+.. rubric:: Dimensionality Reduction Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   Intel® Extension for Scikit-learn TSNE example <samples/tsne.ipynb>
+
+.. rubric:: Jupyter Notebook Samples
+
+.. toctree::
+   :maxdepth: 1
+
+   Intel® Extension for Scikit-Learn* Getting Started Sample <https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted>
+   Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample <https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult>
+
+.. seealso::
+
+   There are also :ref:`Kaggle kernels <kaggle>` that use |intelex| for a variety of machine learning scenarios.
+
+.. note:: Explore the complete list of oneAPI code samples in the `oneAPI Samples Catalog <https://oneapi-src.github.io/oneAPI-samples/>`_. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.
diff --git a/2025.1/_sources/samples/ElasticNet.ipynb.txt b/2025.1/_sources/samples/ElasticNet.ipynb.txt
new file mode 100644
index 0000000000..29e5dca851
--- /dev/null
+++ b/2025.1/_sources/samples/ElasticNet.ipynb.txt
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/daal4py_data_science.ipynb.txt b/2025.1/_sources/samples/daal4py_data_science.ipynb.txt
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2025.1/_sources/samples/daal4py_data_science.ipynb.txt
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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gRPNPNpFsnKcqAI8E+L1y1MvuNbbCteEHgOunnoFfvrzLUa8/UUgm9u+cPnYofrR6u+k1KgT2fXoSN/xhS/Q6L55Vj3HDqmyvD2s1FR6l+J5lPeFLRIMAXArgDAA1ACqI6Bqb191ARFuIaMvhw4eTOkdtVVlS4+nAWPqXrbp6pzLOlrbjfSoldDP/ZBLJsfMMKQKyJGHBhWc7lmG6we8hNNRUgeAc3ok3p1+/shtf/PmruOqRt3Dez17Bf730D82zn1VvSsbe/OUxuHtti+k6L1m/I+GD0Ugy1z9bG/IKceNfpijF8ttchH2+BOBDIcRhACCiPwH4IoAnjC8SQiwHsBwAGhsbk/LZP+22rwl0Gu8rRq82qChQBRBSRMY9CCfvGxApV/i49YCSSSTbzdMnSxg9pAJhmzp7N/hkwhWNw3HLyiZTmabpNR4Ji2fXRz/AxpDYQkN5p276/uuVVjz4aiu8HgnfPX80BLTUwX0v7kpKX78vFVZOoYd0ryRLLcSRiHysiss0uTD+ewGcR0T9oIV9ZgLYks4THDlhL93sNN4XbMXHYshUXb3TDdtQU5VyhU8y4Ry3FSvx5jl/+lm4/6Vdbv9kAIC+l+vpd/bFDRF987yR0dh8UFFx3ZRRaKipMhn+WBQBKCEVv3q1Ne4c4hmGVCusnB68J3rCWLJ+R9oMdSmGOBKR6aq4fCQXMf/NRLQKwLsAwgDeQ8TDTxdOad1MpHudNNiNJON1u/Xu9Nc6JR9TLSW0M9RBRUFHdxDtnQGT4qZ+7PHDB8Y9ZrwP1lWTRmDZhlbXtfxAxEC7yAs8uvFDCCD63vz2rx+4Pkc83OjrO13/eO+xUyXU3WtbEEzjSrKvG/+KtSKm1Mpvc1LtI4S4E8CduTh3urEzlh4JkCUJPtm9B5HMMjz2tU7Jx1RKCWMNdXcoDFUANz75XnReAkg6ZGD3wdKNyC0XjsG9f9mJJOy/KzKR3/dIhOd/cL4rmWX9+rcePIENOw/h064gHnh5l+N1s10hKQJeWTJVSPV1JZmJsFSxUErltyRcbG7JNY2NjWLLFveRoVG3rXf82Z6fz0rHlEysaTpg8WqT8SDaOwOY8otXTRUrZV4JmxbNsK0kcfvavtB68AQ2th7BT5//uyne7fdoOQVj2MQnuzeIOk++9RHuXrsDgEBQEfBKgEPBTl4hE/D2HV8CALS0dQAgNNQMcLz2P37ufTz+VmxfAA279y32XtJXdul+v+3u2URGPFv3HpM+iGirEKLR7mdFqe2TbZyWi26X0Bt2HoIco9/v5N2lU6vHafke1bgnsiQ6ZYkim6d6PdGgInDxgxtx31yzATG2X+wKKlHP8pE3PsDDMSEYo+H3ewhhRUAI7cHSk0cd2DyyhEff+ACPbvwwmm/wSMAD8yZY/vY3/9nuaPgB+/fN7l7q7/ekPRadSogjX3WickWhh7/Y+KcJp+WiU0/Y2kHl2Nh6BIue3QaPZN2c5LQMT1dVQryqEj0ZaIcmemY1xsGwaopF68cHgJ6QGmnCov1evPCOBET2ARACikAgjww/AATCqiV3EFaBW1c2Y2A/HxpqBkTf10SLaqf3LfZeykQsOhXDVYoVMU4UQ/iLjX8GceoJG1sSaqTCJ0MRwtG7S0dVgl21x4JVzRjYzws41Mz388pQoc0L0IydU/kjAMsDxK0RVwGohtfml+l3JqgIfPcPW6AKQFHVhPkLnwzT+5aqFEQqRly/Lz2StrK785J6XD1pZMLfi733goqKGy+oc3XOfCEd3nqxVEux8U8Tdh5+7A2i94R1qgyq8Mu4+5IGTB87NKVKErfYLd8DYYF/f+JdKKqwrby5d+45mHxmdfRc9acPwMUPbkTQ8FrdC3RTAVWMdCeRtPjPiz8T9RTjeZGtB0+gad8xTBg+0JJTScX7tFvZ3fHn7YAArj4v8QNAv/ee3LwXD23YjeWvf4CHXmtN2fPNZugkXd56sYS/2PinAbubamR1RdIGUFFFQsOv05eqBKcmJicddO/9MmH4Kf1M56s7tT/um3sOFqzSxNAU1bxasTs+08vUOq1LWDwv8lcv7zLlDK6dPAL3XPpZx99bsGpbNPTkdG/sP9oNj43s9d1rW3DRuNNc31O/ea0VgbBAIBw2zTmZe9KtMc6Ut+7metmRzfBXJh+ORannn02cJBYqfDK6Q+52FPtlyoimu9P2faOefD9vYnkFksj2xhb6/0Xvd8bja5VBxYUMwNNHafBrJ4+IevF2sgIyEZ55+yNLsvjxN/ei9eAJxyKBQFjFv/9ha1xJkdpB5bYy2V7ZvZRBOqQQ3HaYS5dUit2c3VwvO7LVjyHTMjFxPX8imhjv50KId9M6mwLEaQnY1tEDIoKbqLUgwvr5U5MqlUxEIq9KX763tB3Hdx7fYgr1eGWCRFrzEqecgv7h1Uo+tRWDnjdoqKnClLrB+MGMOvzqld2uNfl1vjV5JH7/5kep//EZREHE+KeQjPDLBAGgceQp0bEKn4yAYvYiu4IKfv3KP22P8dimPfjTe/ttiwQARBvRO3ni1ZV+3HlJvRbqMf5dwn1v4nR4vm5CJ+mMrTuudhNcLycyvSEsG3mFRGGf+yP/lgFoBNAMLSN4DjRJhslpmUUB4/RBAATKPDJCSmLv3y87txpMhWT0eRpqBmDm2KF4fvsn0fErvzAcN80cE/fGbmnrgGTxPLW8QTCsQICi7RCduPGCMyBLMoZW+vDR0W6MHlyBCxtOQ0tbh63xT9Hmpp1UN6PpSW/9vdCrghTFesDYB4LOqq37LA/Tcq9kyTcYPfHY9/HqSSMBoYV6vLIUt8DAjnQUHbh5gKQztm6cs0RkCXGmctxMbgjLRl4hrvEXQkwHouJrE4UQ70e+HwfgrrTMoMAx3lQyEUKKtuO2oaYKQYcPcCzpjhe6vXE0gbNtlgTvii37cdPMMY6yDbHCaEZOGqSZE/HIGx/C7/FYViYNNVXwymZNf48E/GhWPe5Zu6Pg08heSUJLW0fcklo7Zo07Da/vPoKgwaGo8MtYcOEY/OyFnab3I6Sq2H6gA1csf9N29Xf1eSNx0bjTUvZc3Xi+8eLVbh4g6Y6t9652OyKrXfP1yqeS1WzkFdwGZc/WDT8ACCG2A/hM2mZR4MyZMAyLZ9UjpAr4PBKWrN+BTa1HMH+6uzK4eY21afUgnPR5jDdOb9jGanzixW/N4Z7UIQBBBbbx3upKP+6/fDz8Hgn9fDL8HgkPzJuAS8bXwOfN3zxChU9GmVfCtZNHwO+R4PdI8MkEr2xeIWnvDVli0Hb4ZMJdl9Tj5R9Owz1fG2d5XxVV4JLxw7B07nhTDHrxrHosWb8jbky9urJvPaPj/b6bePWcCcOwadEMPHH9JGxaNMOS7M1EbL260o9pY4Zarle+ibhlI6/gttpnGxE9il7Z5asBbEvbLAqc9s4AlqzfgWBYRTDilC18dhvWzZ+KZRt2JzSUuqft9MYmm/HXb5ybVzRFQxSqADa1Hol+wOKVY8bzMOx+r9wjQRFwtdLxSITvTjsDv//bHnQFnVcmTp7lvM/Vxt01m0v+44Iz8fUvjEB1pT8aNtt+oAN3rumNr3tlwuLZ9dBkLZxDfcb9HkajOK+xNloyrH9vJ+TnJBKXjXLEZOLViUIn6Yqtx36GCkHELdNzdGv8/w3A9wDcFPn+dQC/TetMChinMEtXUMHSueOx0LDTNTacob/WTQesZGqTp9QNhixJUb38kCJMH8DaQeW21Uh+T3zFSq1axGy0BAF3XtKrLtodCjvG/MOqwH+//oElbq4/cNo7AybNHD309Ny7+/Cn99qwsfVIwr89V1T4PRZpjyuWv2n6W4UQuGdtC3yyHO1oJpO2m7kssqpZPLse42qsQn3tnQGs2LLfdE6j4xBrSGNXCV0BBdsPdCRUYe0r6Y5X9zW27vQZKgQRt0zO0ZXxF0L0ENHDAJ4XQvwjIzMpYOLF58YPHxh9elf4ZLR1dFvijYGwYtvRqi8Z//1Hu+GTJXMVT4IPoEcirP9+fIG2ja1HTO0wPRKiH6aLGnpjyADw5j/b8cMVTZaHnV3CdPGsemxsPYJbDKsVCcCVXxiB57e34ejJzDTiSSfDY1ZLdkYwrGoPQL0+3u+R8Mi1jaipKovqH8Wr0XdrVKsr/Vg821rVs2T9jqTq+VMh2S5vmfS+i2U3biZwFUAlojkAmgD8JfL9BCJak8F5FRSJ4nN6bLTu1P6meKM/EguWJMLsZRstcdG+1FMn+gA+uXmvxQiXe+W4VUftnQHcurLZZMyJCFPqBpv+Tt1bmT2+Bvdfrv2t/RK0a9x98AR++EyTaU4qgCff3lsQhh8Avvfku6b30Km80IhPllBV7kXdqf0Txt+TTQKOq6myOBXZaE3oNl6djXanpdie0S1uwz53AvgCgNcAQAjRRERnZGpShUgy8bk5E4ZF5REAEa34iPVI+pLxj1dN0d4ZwEMbrJ2qYpPCsbS0dVi8+JAi0NLWgWljhjr+rU4VFkbypa7/rCEV2H24K6XfDcaE1vT3YMGqZsikheBUIUwPuGQqOJItsawdVB4V00vlfH0h0echWx45i9E549b4h4QQHWSu686Hkuu8IpH4llHauCuowC9LJm0c4xI+2qkrUrWRbDvA/Ue7MaVuMDYtmmH5ANqFhABg/vSzEhzbfmvrB4e70FATcPxdY4XFwkhde75q9zsZfp8suUpoq4pqeg/3tJ8EQNEPy6zPno4Xtn+SUn094N7J6Mv9ky7ixauzpY+Tjj0JbihEeWe3xr+FiK4CIBPRWQB+AOBvmZtW8aA1LWkBoHmGxqSek0di6dTlkAC0w02C2M4b8nskfHXcaWjedyzqFcXezDVVZZBJa6No5L4X/4Gf/2VnwmS0vuLRmsTsdL0PIh+YdlY1Xt55OOHrgiqw+YN27Gnvst0Lsbr5Y/gkIKQK3HrhGIysrjC1x3RDoiRgX+6fbJFNjzzTVTOFKu/sqpNXpNn6HQAujAz9L4D/J4ToyeDcouR7Jy8nnnzrI9zx3HbbnxlrsWM7gKXaLSmZTkuxnZyMctM9YQVCCJR7ezdg6a0bAThuTEo0T31zmEwSAmEFkkSRev/CXESWeSXba6HLY7jZC1HplxFWzSWdffEi2zsD+OLPXzWt6uJ1hUumx3C6SaWbWL6R793N+tzJSwhxEsAdRPSTyNeMDcYPDgDcvW6H42tlieD3SFg3f6qpyqN537GUl8PJLKWN3lCFT8bsZRtN8VdA24AFaNr9ACX01O3CVkaJ695KHi2prCoCHrmPKmk5wkvA3Im1eGKzdc8BgaC6VDXtDJi1ZXTJh0T9mZ14cvNeSzjPaXe3fp6gomD+9LNwSoXP4oxk0hgXQq19IgpZ3tmV8SeiLwJ4FEAlgBFENB7Ad4UQ/5HJyRUSsUu/f/viKMSza10BBXeuaYnGffXa674sh5P9XT18YPfAMaJ55om9WKew1b2XnYOB/XyW6iIBazObQiEkgK5AyPZnsUldN8RKPujvxR3PbbddHdihJfJ3W8aDivkesEu23v/SrujPs1kSWQi19vEo5ISy273yvwTwFQDtACCEaAYwLVOTKjTs5Gl/+9cPbJt7GGWOu4KKrbRBvDI5o0xzrGRzslvC9d+v8Ml91t/3yRTt8mUn1dt2tPgWjGuaP7Yd/2xN/2huxy3xJB86A9b7xA4tkW8tqZ0/vc50D9iVP9rBJZGJyYYMQ6Zw3cxFCLEvptonZRlKIhoIbSUxDpoDeJ0Q4s1Uj5dr3HSu8skSbv3KGJzSz4c717SY6undShsYPeqesAJVFfB7ZChCxdK54zFnwjDXS2n9WLoY3dfOrcFz77WlFH+XJeD5H2ibw+xWETIRWlMsn8xrHGRGm/Yfhy9OLwOvRFCFgCSRpvwaWR011AyI+xBOFE6w80J9MjB+eJUpqexm/wGQu41ZhUahhq/cuif7IqEfQUReIroVwN/7cN5fAfiLEGIsgPF9PFbOqR1Ujp6w87Own1fGo99sxA3TzsT0sUMttddBRUFHdzCu6Fbs6iKkCChC0yMPhAVuXtFkWgHE2zBkPFZXUEFQEVix5QD+44Iz4xotnW99cYRJdO2X8yZEdwXbGZauoIKn386POv5UcIreOaVABIDrpoxCmVeyXQGEVO29kwh46Opzo6JmRi/Sbsd3QFFtx3VivVCPBAgQbnzyPdMmKv11foeuNLpAXa42ZhUiiT5z+Yhb4//vAG4EMAxAG4AJke+ThoiqoIWMfgcAQoigEOJYKsfKJ+JVTakQaKgZAMD+A6oKWD6gsew/2g0RRx8/rAItbccdf24MEe0/2m3pAgUAv/nrB7hzdn10bn4PQY5p+eeVCd+fMQZ/u20Gnv7OefjbbWY1RicDZhRx08nXVK9XAm6aURdNRqeSlWioGYBNi2Zg+Tc+B59D8scny6gq95kMxpwJw7Bu/lTcPacBE0dUmV4fCqu2O8GN6EqZD109EbIkIaSIaPhtwaresNGcCcPwt9tm4pYvj4Hf0xuy+Mm/jsNT3zkPmxbNwJS6waYQ4+u7DmPhqsTdt5jCwG21zxFoSp7p4AwAhwH8f5HE8VYANwkhTHEBIroBwA0AMGLEiDSdOjPsP9qNcq8nWh2jU+6VIACLB2XXRUtv+qIn2fTj6stIreNTIjMkIsJoxwEINNRU4WhXEI9t2oNVW/dGtfMXz65HyMZt9cqEccOqTBvDNrUeMfXpXTrXLFsRS3tnACOrK7Bu/lQ07TuGO57bbisbrc02/5AJeOGmaegKKnh044cIx1HejMeJnjA27DykyXh4ZJMGv45dWMUYjouV2hBA1ODaJWKN4Ziqcq9lI18grOKpzXvx/ZlnAdDev+/PPAtXTRoRN8TYHQqDSMtHuKkkKhRKPXzlttpnNLRQzXnQ7sE3AfxQCPFBiuecCOD7QojNRPQrALcBWGx8kRBiOYDlgFbnn8J5soZTDFVRBe6c0+BYoXHoeA88EsHoN3klCU9u3ovfvNZqaQjvVFcOaIZ736fduO737zhWmugGaMm6Hbj1wrPx0xd2mn4eVgQ6ukNRQToguXim0WAEwmFcWH+ao+HPVwSAnZ8cx9jTBtg+IN1yu0FQzaZfOvweKZogN26sc9PgJVHppl4ialeau2zDblw1aYTpd2MrbuyqgQCBkM2DsFAqW2Ip1I1Z6cRt2OcpACsAnA6gBsBKAE+neM79APYLITZHvl8F7WFQsOihjthwbFARWLJuh2NT6rvWtli8u6Ci4KENrbYN4e0o92pNQ+66pAH3rGtxVWLolSRMGl2Nn/zrOPhkQrlXhlcmKKqKG598F1/8+St48JXdrnMIgDUnEVSAde9/4vj6fEUVwPynm/DVX7+Br51bk7Zj+mSKhtJu+bIWNhOAKX7+1Oa9rqpwYnNEdtVmS9bvwHVTRll+1yfL0Qqe2GoxHTfVQP3i5AXyHbfN44sdt9U+/YQQfzB8/wQRLUjlhEKIT4hoHxGdHZGHngnAeTdUgaBVwtuXfzg1pTaiN++48YI6LH/9A4sUc1dQMWmUBBUV100ZhclnDkZDzYBIHF+CmyIs3Vvb094FLfQvovX2eujq/pd2YdmGViyd684jclPxVEiEFIHVTR/jP786Fkv/9x8IJehHnIhLxtfg2smjTJveYr3rZRt2w00mJKxqOSLjqtBuo9HkM6vx2KYPbdsVxvN8E1UD+T2Eh6+ZiIaaqoIz/EBhb8xKJ249/xeI6DYiGkVEI4loIYDniegUIjolhfN+H8CTRLQNWvL4pykcI2/QP8h2G5Z6QuYKDTuvqsIv4+45Ddi0aAaumjTCcdOInsz7zrTRAASeeGsvbvjDFmxqPRJRcExseH0yTPX4gbCw3Y8AaDHiBau24fVdhx29onTuFcg3ZInQE1YhSVp5JgDbtoxuWN3UZhHXi70PfLKMz5xm7qVwzrABlvMpqrCsCu3umYaaKtt2hYD9Xgyn/SIeSQsr6sdYOnc8po0ZWrCGspA3ZqUTt57/vMi/30Wva0sAvh75fnQyJxVCNAGw1ZsoROJ5vX7ZnLizu/EUVWD62N4PUyIVwt+81opAuLchyMJnt2HTohlYOnc8bnqmyTGROmvcabjna+MAABt2HrKt+IklEFbx73/YChUCN39Z26cwIdKbQPcePRIhqAh8bUIN/vzu/rxV7EwWRRV4yKYN59PXT8KVj25OandyWDVLX9vdB4Gwgqb9HaaxbQeOo8InOZ5LXxW6be/oVkIk9vcAq9BfoZItpc98x63xXwStLv84ES2GFqNfIoR4N3NTKxziLZNJIpNHUV3pt/ShNTZwN1bL2HV2irdknVI3GD6PuSLDJxP+8+LPYGrdYIvBjte4xcjJkPa6nz7fmyCe1zgMa5o/NoWvVmzZDwlagrOPUZKc45UJl00chtVNbQgYQmkeScL2tuPa5iybCp749D5s7QzQZefW4sm3rVpB8aq8Qqq2skymvaNbzzf294rJOBbqxqx04jbs86OI4Z8KYAa03bncwzeCcZmsd+fSN/jEehTtnQGs2Gr9oLZ3BkwbaGYv24iP2rssN2W8D66u02/E75Fx7ohBqDu1vynOrAuKAb2ben7yr+Oidd+JOm+t2HIAkk18WkXhGn6ZgGVXnovv/ctoSASsaW6zPCBPhhT87IWdtv2PE6Hv9dDRw3hPXD8JmxbNwL/ZJGgBYOFXznYMwdx72TnoCipJdasqZEmCdFKIG7PSiVvPX/8EzALwiBBiPRH9vwzNqSCJVcl06sfq5LnbiXrZ1XPrH1xj7b3xgxv7YAgqKjq6Q9Ga5thzV/hl3H1JgynsdNWkEQk7b+nHLgYImkTF3XPGYexp/XHziiYEFYGAw67tQFiFVyb4Pfr37p52R7uClvvB6F1XV/px7eQRpvDNtZNH4IZpZ+KyibWOIZj2zkBcT96unp09X8at8T9ARP8N4MsAfkFEfrhfNZQMbhQKtc1a1g+qLuoVq4mzYechk2EG9KSLAIS5uig2lNATVqLlm3rtd6J8g34cY+ctgkB3yGrgrpsyEsvf2JPosuQ9AloFzeLV2yERIRyzdCnzSoAAegzhtJAi8K3JI/HEZveyFU37jkVlMJy459LP4trzRqFp37FobgWIH4KJF8OOV9WT6H4t9U1QxU4yCd+LANwnhDhGRKcDSKnUs5TRP4gUkYLQdV+cRL26ggruWtuCH63eHv3Q6qEbzds0a8EbE3xRz13pLd9csn4HFs+ux5J1vZrti2fXR8MDdpr/J3rCuHvdDngkmIzil8YORc3AcvhkKthmLLGoQpNjjiUUVuGRrb7OE5s/gleyPiycGFXdz9XrBlX4cNap/TGowhcdi+0V4caT70ufXN4EVfwk08zlT4bvPwZgr2fL2GJX36+qIqqGCfRW+Ri39sc2+7AL3cSuEKor/agq98Eny9GKIEALL42r6ZVv2H6gw/QgMD5g9PDVkvU7TH2GdV7eeQgv7zyUkWuVb0gS4bopo/Dbv5o3tIdVuDb8HgnweuLnUQB7o6t3UTNKLRjVQJ08+VTr2bPVXF0/Vy67iZUyriWdmdTQtXY+OHwCnph9/n6PjLaO7mh+QPfeNuw8hLvWtpiSsvqH1kk1M3aFUDuo3JKU7A6FTR+oK5a/afqA37qyGR939OCXL+/SJBoU1XVHqmKGQGioqerTKscjSwnryO2M7oJV2wAIBMIiRmrBrAVlZyRTrWfP1iaoRA86XnFkFjb+GWR10wFD60IrPWEF33l8C3yy2YubPnYofrTa3PtXl/M1xnfjrRAAQOu/0GusjP0Y7D7gQUXgZxG9n2LZqZssdg3qg4qKm555r09VTItn1yc0nLarOokiuR375HM8o5xqPXs2NkHZP+i0dqGBcOZXHAwb/4zR3hnAwlXNtoa/wi8jrAgoqoqAgmhoZsGqbRjYz4eGmgGWTTvhsIpZD74RbdqSaIUAaA1kjGJcZR45aijcNvQoFWSJcM+lDagq8+CmPzZZtPr7mtYYnmIbTkWN30IzkVFOpaonlYdGsqEa+/ClZFG3KEXZhWzBxj9DOGntlHsl3H1JA4YOKMONT75rkoHWd9MqQnswGFGBaNMW3ROyWyHoxuAv2z+x1KgbDYX+Ab9lZXPB9tFNJ8uvmYjOoIJbVjY7NmmxwysDALm4hol3UzsZXQDRMbuYfyLDqOcCdCkONwY6VTVXt6Ea2wedUCOrnF5KUXYhW7DxzxAVPtlWAlcVwPSx2hZ/O89b303rhN60ZdqYIXGNxZL1Vq28OeNPt/QVkIgw/+n3XP9dElB0ASGJgO6QYqiicke5V8KPZ9eb5Jvt8EjWDV5OOBndvkotpGKg3ZQup5ocdvOgK1XZhWzBxj8DRBtyyJLJwHtlMjVD0W9+iQgnXUotaPQaKDtj8fquQyAbT/O599qw6KLPmD5Mk8+sjpRxujszFbj1L/fK6I55wKoC+OGKJssV88laGadTrF8VAoGwCp8EGBuVeSLRC6/c21/ZrpIFsDfidka3L1ILmaze6Uty2M2Djg1/5mDjn2bsSjq9MuGX8yZg8pnVFs9br8mPbcIiSwSZBGKfCV5ZqzwxYjQMq5sOYOGqZlsP1itbP5TVlX48MG8CFqxqhkwSwqoat6Jl/vQ6/OqVVlfXIl/Qk7jeiIdvh90wEcEjWd+Dfj4ZwbACVQD3vfgPxHao9MgSnrjuC9jTftK0UWt10wEsWNkMIoqG9cq9Hosnnu5SR7sWoEIVaYml9zU57OZBx2QGNv5pQv/AdnSHLJ5QmUfG8FP6OVZkxBpzAIAQeOCKc9HS1oHHNu2xbaNoN4d4oQtFCNsP5ZwJw1B/+gA07TuGMq+MW1c123aTmtdYi6H9y5wuQd6iXw2bTcpxufLzw/HsuwdMLRgrfDIWfOVs/OyFnZH2m2aNJEUIzGusxTWPvW0KXUypG4wf/rEpsoronYie89E98Y2tR9Je6mjXAjSgiLjN4N3CCpmFCxv/NGCMpwYV1ZKsTaSzYtcDWBGaQVCFwI9n12PcsKqEnqCTtHSZhwAixw9lovkDgE8CnntvP559d7/lZ/kMQfP8UynTHDW4n9WrVVQMrvRb+uPqGkkThg/E7GUbLSGWO2fXx52Dpu90PCPhma6gYmkBWuaVXKu6JoJ1ggqTotTnqfQlN94XYlvCBcIqiAh+D1kUE42qnV/8+avRVolOZZcng0q0JZ+bD5XTccIqcPOXxpjCCnr7Prv52xmpoAoEFSRVCZNLPJIWs7/94rEp9xcIhoVFrVWSCLesbEJPjOibrpHUFVRsQywfHOmKey7tfRNJqXO6xSkE09cqGuN9VOoKmYVIUXr+wWBy433Bztsu88j42f8Zh56QGo352iXdtFaJu7F07vje5C/IUvFjl0CzW0HoS/BbVzab4vZhVeCnL+xEhd+DyjKPKaxw4wV1lvkXqiSzjkzAj2bVY2rdYLR19MAruynFtPLAy7uwadEMrJs/FRc/uBGAiHrPHklrwu6TzaGOo11B2xDLeWecgkfe+NByjn5eCSp0faeqjGyuykRohrV/Cp+iNP6hJMf7gp233R0K45aV20yGwa7PKqDV7uuduDYtmoGWtuMRKWWDgmSMAYj3wdO2BNkburvWbodEkmkH5bINrRAu2j8WEooAfv6XnQgr2iqsL/sYdK/bL0smjaNyrwcPXT0RVeVe0wPYKcQyuH+ZRa5ZIuB7F9ThqkkjLBVg6YyfJ2oQlMrxsqX9w2SOogz7OH3UM+HQGhtjVPhleGUCkbZFPVGfVR2jZz9tzBAsnevcaCM2TKMfv/XgCby+6zAWrGy2rVwBAA9JmlyAAZ8sYWR1RZ+ugUSat51P9IRUhFXENfy+BJPW+y87VbQ01AzA+OEDASAa/ogXYrlp5hjTOVUBPPSauXIqtsFLX71pNw2CksWu/3A6wlNMdilKzz/bRKWP17ZAJjLpvgO9fVb1JiyBcPylfbwEmlNS9+JfvwEhgFCcmI0iVChhs8HrDinYfSh+PDoRqgAuahiKv7QUlsrn3M/V4k/vHdBE7MIKVCFM5bZ6/+W6U5PTy3d6bfO+Y/B7ZFP1kF1IL12ljpny0LkBenHAxj8FYuPt7Z0BTfpYsddh0T8Y44cPxJS6wXhq814s27DbJOjmttbZ7oNnV5ZppJ9Xq+IRoIhWTC+x36dKoRl+ALhuyhm45cKzo/LVF/3qddPP9dJYY9ikraMHgEBDTZWjcdVDeLEP72wbzUypc3J5Z3FQlMbfQ4BdqbsnDaEJO09vZHUFZLIevJ9PhirMbRarK/34/syzcNWkESnpmMd+8HTZZacdut+7YDQmjx5sySPoFHhu1xFZq26N6uB0BcOmRPakMwahK6hgUIVWB7+x9YjlGESEv2z/BEvW77DV1bFLluvG1a7yJRNGM949k8mHDZd3Fj45M/5EJAPYAuCAEGJ2Oo/tFObtq36Zk6f3wy+NsdRM+z2Eh6+ZiIaaKsfNXcbxZKonjJuyBvXz4tuPb7V9nU8mXD91dLSxu53xL1ZkScJT138BJ0MqXv37Qfz+TXO7xc0fHsXVj76FroDi+AD0eSTcvbYFQcVeS3/ZhlbEPj4zobLpRKJ7JtMeOu/ELWxy6fnfBODvANwpXiVBphK+dstoCcC9f9lpee2PZzdg2pihCZUU9WYvCyO5ALvYbKx3Z/zQBxTVVoNe/3s3tR7BlLrBJSffrAoVVz66GR6J0O0QFjNKYdsRUgS8soSgjUAfoCXLb5g2Gg+91pqUcU2H0XQbz2cPnXEiJ8afiGoBzALwEwA352IOqWC3jD7pYFiGn9IvoWem/1yKNLAwoocPYrf73/ylMbjvpV0IGh4UToSU3jJSu/r/YsBJZ067nKJPZZ5fm1CDNc1tjj8PqSqumjTCNoSXaZKJ57OHztiRq1LP/wKwEHH0IYnoBiLaQkRbDh8+nLWJxaO60o/Fs+pdvfZ4d8i2JLO9MwDA7LnZyTiHVK3MMPYYP31hp6WnbryKRSGADTsP4URPOKXkrt8j5V0Zp5FMrmfWNLdh8ez6aNmtR9KE9WJLcHOxu5Urbpi+knXPn4hmAzgkhNhKRBc4vU4IsRzAcgBobGxMympV+iR0xkotRsb7yrhhVaj0y3FDBl6ZMKDcG9czcyrZNCaJu4KK7WtiiefcBsIqFj+3Hd0pxvsVVcUfbzgPV/3ubdtG7n3F3Ggys/g9BAGKevReSUJnIOx4/tiG96lq6fcFp4QuV9wwfSUXYZ8pAOYQ0cUAygAMIKInhBDXpOsE08YMxvPbraWH08YM7vOxaweVIxzjQXskLcFoVN6sqSpDIGztpFXhk9G875jtpq/YJHF7ZyAtsfpUDT+gVbycMaQSX/+8ua1kukhk+Ms8WqI6mQeELuRm/B2JgEeu/TwaagagutKPRReNxf6j3dh+oAN3r90OwNqcXfek+6Kl3xcShQ05ns/0hawbfyHE7QBuB4CI539rOg0/AKgOTrnTeDI4eVy6Lj9A2PfpScxethGSpGVi/TKBJMK8xlrMXrYx+nvzGmuxYst+03GmjRlqey5ZInQlSFBmAkUReObtvXjirfQb/kTMa6zF7HNqHMtUY/FIBFkiCAGoMQp0EiFq+IFeA37F8jcjev29hr/MK0HElOhmG7cJXY7nM6lSlHX+r+2y33DkNJ4sdh5XtHsXkaXsUwXw8JXn4san3zN9mFds2Z9Qb8V4ru0HOrBk/Q4IVSCgCPg9lFTbwVRQASx9cVdGz+HEmuY2zD7ndHhlgkHtGv28MibXnYJX/t6bC5JIE7CLXZXplHs9aGk7btLicQq9ac+B3CU62jsD2LDzkGXvCDczZ9JJTo2/EOI1AK+l+7g9Dg6y03gqGD0uu+5dRkKKwHeffBexrTN02QddHybRucYPH4hJZ5yCpn3HMKq6H/a0n8Rda1sSlizGwysTLjt3GJ7Zkn86/V5Jwt/+2W75+3rCCu69bDyOdgXRtO8YwoqK2xL00e0OhfGdx7eYxPacSmB1eY5ciJXpToRHsjoRnNBl0klRCrtlGzuhq1jCirBI/RpzAHoVUDxWNx3A7GUbcffaHbjmsbfxaVewz0lYVRVYuTU3hv+Kxlr442y7DioqfvfGB5ZxVQBHu4KoO7U/5jYOR82gfra/r1fm+D32YnsAoqJ8/bzWrlaZEisz6uDHjutOhPGBV+GTLQJ/DNNXijLsky7c9lJ1aqISS5lXgqoK+CPyALE5gHi7eu1iwD99YWdUJdInWxOWbshl2f+sc2owpW4wbl7RZBFUI4lw4wV1WPZqK+wKOpv2HYv2xm2oGWBpQu+RgBd+cD66ggo6uoO48cn3ojtzgV7D3ttHObGUdjzc3ivxkrh2YSi9Q9j0sUPZ8DNphY1/DPqHWI+vxyZ1E5Xd2cX8dYQQeGDeBAwo96Kmqsy23V/96QPQFVRQ4ZNNuQC7JtwAogZfFcB/fnUslr74jz5tbMomNVVlmDZmCE70hHHX2hZ4JK2x+fwZmvYRADz4qn2+YYIhVBbbhF4RKpbOHR99ONhVTRkNu1FK2650MpFhdyvNkSiJa+dE6B3C2PAz6YaNvwFjvFVfdusf0h/+sQmSRPDJEhQhsHh2PcbV9PbVNSVm2zpwz9odEEILXfhlgiIEVAHc/qf3HUXBhCpw8YMbIZGm1Kl7wPdedg7qTx9gCRsZCasCY0/vj7dun4mWtg6sfu8Ann3PeXdqtondiavLJeuKqCGldzfuQ6+1Rhuc3Hf5BNz0TJOpbPPaySOihl0nXtmj25r4eIl8J8OejGxyol25XLvPZJOiNP4DyyQc67GGCgaWOcflEyVtFaGVPYYiOi93/Hk7KnwylEhJ4JwJw6If4D3tXQAEPJE8wHVTz8Bjm/YgEFbjioIFYiShA4oAIhINy7/xOUuHKCuE6ko/aqrKsWbbx7av8EgEVRUZ3Rlr5MrP1+KyibW46tHNprAUSYTaQeW2KxqhiqhB1A3ym/9sx5HOHkytG2Ix/Drxyh7d1sQ7JfKdDHsyMgtuduVy7T6TLYoy4evUZDxe83E3SdtYuiIN1u1kGwJhgZMhBUEF+N3GD+GVrR205k8/KyodEE9CwStJON4djttbVyYtjLK66QAu/vUbtqEfjwSoQmStitErE8YNG4irf/c29JP6ZTIlLyt8sm3P2wpfbwK2utKP2eNr8K0pox0NvxuSlWFw07EqGZkFY9c3uy5tqc6TYVKhKD3/EzbSDvHGAfdJWzsSyTZoypDWSh9dFExPNioOlTtaT+BmkNCOUeaVoKgCqiogS1qiV5IIsx7ciLCiOiZxs63ovOArZ+OutS2mB5EigL/Mnxptat+07xj8HrPctE+W0NbR3SdDnw7cGPZkQzXs2TP5QlEafw+AsMO4E7po2x3Pxa8Xt8NoEGyTdkLgzkvqsWTdDlsDUVXutdXb90qAJBFUAdPPVFXghR+cDwD4SqT7VMihi1iu6OeTcfhEj2UFElYF2jq60fLxcSxc1WyraBpUVHzn8S1YOnd8n3vY9oW+5AoSHZeNPpNritL4Ozm4iRxfN6JtHpnQOHwg3tpzNDo2r7HWJBsQazAWz9KSw067ee0eGH6PhEeubQQgLGWKfo8cLWGMF8rKJSeDCv4Q00BF53h3GLdEyzvtH1iBsMjJJqtYUskVMEwhwMbfgJ1omxGfR8JT3/4CrnnsbdP4ii37cdPMMdEPv50kQ/RBMLtXEjreA+Pey85BQ80AtLR1WJqJ6CuNju5Qgr8oM0gR4bRE9NhIT8gEnOgJuQpB5YucARt2phgpSuOfKkYjDJgbo3tlwn1zz4HXI7uq7jAKhxmrReyqhACrh7mx9Qim/OJVeCUJqtCSteVejyn0YLe5KRuoApj5mSEYM7Q/Hnnjg+j5ZQK+/vkReK7pgGWvgz5Pj0T48Rp3oTW7xKne+Uxvop5Oo+x2oxbDFANs/GPQ++Ne/Os3TOMSAVPqNEloJ088FifhMN0wxoY1dA/TrsTQ75Hw0NUTLcqUD8ybgB/+sSnrO3Vf+fth3H7RZ3D9+aOjaqYNNVpHzmffs8pF6FGteHsV9MSvcX9DbJ/jWwy7gb0y4f7L05MXSKaHMsMUA0Vp/Ct8hK6g1chU+NzVOHYFFfg9MoKGOLtPltHSdhxN+46ZQh4eCY7VHbWDytETjtP0xVA2aPQ47R4aPllCVbnXcp4pdYPhkSXHSqFM0rTvGOY2DjfJUAOwTZzHzs4nASoIXllLaP94dj3GDauy7GzWae8MYOGqZtMqJ6QILFjV97xAMhu1GKZYKErj7yEJgNXoauOJse3VGwzj+v95x1KyKUtSdEVgRA9PqHGC4yFVxfYDHbhi+ZsWGYnYh0ZPWHFeXcRIHmeLMq+M9s6AxUC6SZwHVaCfV/ubF1x4Nq4+b2Tcc+0/2g3Z5n2VJYqbF3ATykm0UYvDQUwxUpTG38nbjueFG9Fj/7esbI6WKuo7fGPxydZ4v7Exu12Uo59XhgqBxbPqsWT9DovHuW7+VMtDQwj7h8j2Ax22RrbMQ7YJ11RwEo1b9Ow2S+4CsE+ce2WCRIAnImMNINq7+Kcv7ESF3xP3AVA7qByKsK5uFFU4iq+5DeXEq+fncBBTrBTlDl/HcvckbOGUusGQXESJYuP9iRqz+z0SHv7G57Bp0QyMG1Zlu4P0sU17LA+Ncq/HIi/cevAE7l7bYjmHV6K0JYHLvBJumnmW7Q5kpx3O+492mxqfl3kl3H/5ePzttpm4e06Dafeuzt1rW+LKWldX+rF07nh4DJfLKxOWzrUPuRnfB6OMs905nHbeAnB9DIYpNIrS868s8yBw0hoHqSxz/+fuP9oNovjtxf0e6/Z8N43Zp40ZEh2P9TiDiopVW60tE4OK+SGzuukAFqzaZuuRh9zUYbpEUQV+9cruuAllPUSysfVI1EsOKgqum3IGJp852JSknj52KP7TZiOd12YFFYtRfjlRtU8ymjvGYxvDO837jiV1DIYpJIrS86/w2Rt5p3H718qOImoeCbjly2Pwt9tmWEIAdiEEn0xY+JWzsW7+VNPrqyv9WDy7Hj6PhAq/1rBj/vQ6+D3Wec6fXmcRHOtrIxc7ZDI2QdF62SbqExBUFOz79CQWrmqOesmBsMBv//oBvvP4FmxqPRJ9bXWlH3deUm85hiKcwzdGdPnlaWPiyxwno7ljPLZRUyeVYzBMoVCUxv9odzCpcTu6ggr8DmprskRRyWEdvTsTAFMIQatmEbj/xV2YvWwj1jQdiP7O6qYDEckHQiis7QS+atIIm92+FNW3B1IToXPLH284D2/dPhNPXD8Jj1zbiHKv8wOzwidHxOK0XIVdP+FA2BoquXrSSPzka+PgkyljXarciqglOsbiWfUZnSfD5IqiDPt45Vj1eOO4O2oHlYMksm115ZNl09LfLim4adEMtLR1aN2hFOBEpBxHLyEEYJGQXrJ+By4ad1pCPZm+iNDFwysRvB7ZtN/A7jx+D+HHlzRg+KB+0e5XISVxSavxb7j6vJG4aNxpGa2i6auI2uqmA1iyfgd8Hk2Y785L6jnZyxQNRen5l9n0Y403bofuOdr1mDUu/Z0SiwBQVe6DTzafUzeELW3HIcVoKxtbC66bPxV3XlJvCRUZ51bmlVDmTf4t9HskU+JUR5K0B4vTKsbvkSLhrpm4etLIqCBdIuJJHGdaujjVc8T20w2GVSxZt4OTvUzRUJSe/7GT9uEdp3EndM/xqc17sWxDK3xyrycOAM37jqGjO+SYFHSKGW8/0IF71u2wqFnGKy/UPVh9E9SUusFYN3+qdScyALJfsETxyRLOHzsUz2//xDR+xeeHm5K2xlWM0XvWHw4VPtlWkO66KaPw2KY9puuVLgOfrZr7ZBPGDFNoZN34E9FwAI8DOBVaKc1yIcSv0nkOn0w4GbIL1yTfxaS60o/vz9R6ytrp7gQVFYpDUtBJ4XPJeqvh93vIUl6oG55bVjZDIoCITO0db7ygzrITWQVQJhOUODX+QUXBKzsPWcafeXsv/vjOfgTC5n0HmxbNwPhIz9zYB9O8xlqs2LLfUgd//fmj026ks1lzz8leptjJhecfBnCLEOJdIuoPYCsRvSSE2JGuEww7pR+OtXXajqdKPN0dr0zwe7RcQKynGxt3tvMo+/lkPHzNREwbM9S2vLBXE1/7V2/vuGzDbti15TJu7ir3SgirAkKIqDDcjRfU4bd//afl9wgSZMk+FOX0t6/Yst9WqjrdSpjZlmDgfrpMsZN14y+E+BjAx5GvTxDR3wEMA5A24y+RvYfvNJ4Mdsa7zCPjoavPRVW5z9bTjTWEsR6lKrSadSC5ZK5PlnHDtNF46LVWSEQ4GaOkWeGXcfclDZg+dmh07rWDynG0K4gHX91tOZ6ACkU1x/CN3q5TKKQrqERXBpkiF2EY7rrFFDM5TfgS0SgA5wLYbPOzG4hoCxFtOXz4cFLHdSqDTEd5pFM4oKGmylViMVEJYuzPnZKz+nmvmjQCmxbNwMPXTLQkpxVVYPrYodGHz/jhA7Gx9QhmL9toWS94JOC+yyfgisZa07ixUU0uQyG5Ojf302WKlZwlfImoEsCzAP6vEOJ47M+FEMsBLAeAxsbGpLasdjjU8zuNxyM2wZiOcEAijzL255taj5h6DNhJHk8bMxRL546POy9j6ETHKxN+OW8CJp9ZDQDR8+gYG9XkMhTCYRiGSS85Mf5E5IVm+J8UQvwp3cf/+FhPUuNOGBOMQUXF/Ol1uGrSiLSEAxLFxI0/N57PSfI49nV2P3cKWQ0/pZ9rOYNchkI4DMMw6SMX1T4E4HcA/i6EeCAT5/B5CSdtql18Xvcxf7sE4/0v7cKyDbujjcWzaXzcJlDjvS5R6MRtaCWXbQ25pSLDpIdcxPynAPgGgBlE1BT57+J0niBoU+YZb9wOJwkFvbF4IW72STbfwHIGDFO85KLaZyPs6hPTyOkDy/DPI922426JV3VTyJt9ks03FOLfyDBMYopS3mHk4Mqkxu3olXewXqJC3+yTqIKFK1wYpvgpSuNfN8TeyDuNOzFnwjD87bYZuOXLY+D3EIdCGIYpGopS26fGIbzjNB4PO3kHNvwMwxQ6RWn8hw+yl3FwGndDIVaZcONxhmGcKErjf/RkKKnxYiSbImhuHzLtnQFXLRgZhsk8RWn8R1Xbe/hO48VGNkXQ3D5kVjcdwC0rmqKN5b0y4f7Lx3NzFIbJEUWZ8H1xx8GkxosNuz0KenlqOnFqZBO7B6K9M4CFq5phVLEOKQILVhXmfgmGKQaK0vi/89GnSY0XG9kSQXP7kNl/tBsyWW81WaK0P5AYhnFHURr/aRGRMrfjxUa2duq6fcjUDiqHIqwb5hRVFPR+CYYpZIoy5j+82r6e32m8GMnGTl23SpvVlX4snTseN8fE/JfO5f0SDJMritL4T3BoLOI0XqxkozzV7UNGfx1X+zBMflCUxr/u1P64dvIIPP7m3ujYtZNHoO7U/jmcVfGSjOLotDFDsjAjhmESUZTGHwDuufSzuPa8UWjadwwThg9kw88wDGOgaI0/oK0A2OgzDMNYKcpqH4ZhGCY+bPwZhmFKEDb+DMMwJQgbf4ZhmBKEjT/DMEwJwsafYRimBGHjzzAMU4Kw8WcYhilBcmL8iegiIvoHEbUS0W2ZOs/lv3kDZ96+Hpf/5o1MnSKttHcG0LzvWF5o3OfTXBiGST9Z3+FLRDKAhwB8GcB+AO8Q0RohxI50nmfUbeujX7+z9zhG3bYee34+K52nSCvZbLtYSHNhGCYz5MLz/wKAViHEB0KIIIBnAFyazhM4efr5ugJw2xGr1ObCMEzmyIXxHwZgn+H7/ZExE0R0AxFtIaIthw8fTuoE7+47ntR4rslW28VCmwvDMJkjbxO+QojlQohGIUTjkCHJyQBPHD4gqfFck622i4U2F4ZhMkcujP8BAMMN39dGxtLGyv84P6nxXJOttouFNheGYTIHCSGye0IiD4BdAGZCM/rvALhKCNHi9DuNjY1iy5YtSZ/r8t+8gXf3HcfE4QPy1vAbae8MZLTtYqHOhWGY1CCirUKIRrufZb3aRwgRJqL5AP4XgAzgsXiGvy8UgsE3ko22i27Jp7kwDJN+ctLMRQjxPIDnc3FuhmEYJo8TvgzDMEzmYOPPMAxTgrDxZxiGKUHY+DMMw5QgWS/1TAUiOgzgoxR/fTCAI2mcTjrhuaVGvs4tX+cF8NxSpdDnNlIIYbtLtiCMf18goi1Oda65hueWGvk6t3ydF8BzS5VinhuHfRiGYUoQNv4MwzAlSCkY/+W5nkAceG6pka9zy9d5ATy3VCnauRV9zJ9hGIaxUgqeP8MwDBMDG3+GYZgSpKiNf7YaxbuYx3Ai2kBEO4iohYhuiozfRUQHiKgp8t/FOZrfHiJ6PzKHLZGxU4joJSLaHfl3UA7mdbbh2jQR0XEi+r+5um5E9BgRHSKi7YYx2+tEGr+O3HvbiGhiDua2lIh2Rs7/ZyIaGBkfRUTdhuv3cA7m5vgeEtHtkev2DyL6Spbn9UfDnPYQUVNkPNvXzMlmpO9+E0IU5X/Q5KL/CWA0AB+AZgD1OZrL6QAmRr7uD62fQT2AuwDcmgfXag+AwTFj9wK4LfL1bQB+kQfv5ycARubqugGYBmAigO2JrhOAiwG8AIAAnAdgcw7mdiEAT+TrXxjmNsr4uhxdN9v3MPK5aAbgB3BG5DMsZ2teMT+/H8CPc3TNnGxG2u63Yvb8M94o3i1CiI+FEO9Gvj4B4O+w6VucZ1wK4H8iX/8PgK/lbioAtOY//xRCpLrTu88IIV4H8GnMsNN1uhTA40LjLQADiej0bM5NCPGiECIc+fYtaF3zso7DdXPiUgDPCCECQogPAbRC+yxndV5ERADmAXg6E+dORBybkbb7rZiNv6tG8dmGiEYBOBfA5sjQ/Mgy7bFchFYiCAAvEtFWIrohMnaqEOLjyNefADg1N1OL8nWYP4j5cN0A5+uUb/ffddA8Q50ziOg9IvorEeWq65Hde5gv1+18AAeFELsNYzm5ZjE2I233WzEb/7yDiCoBPAvg/wohjgP4LYAzAUwA8DG0ZWYumCqEmAjgqwBuJKJpxh8KbV2Zs5pgIvIBmANgZWQoX66biVxfJyeI6A4AYQBPRoY+BjBCCHEugJsBPEVEA7I8rbx8Dw1cCbOzkZNrZmMzovT1fitm45/xRvHJQEReaG/ik0KIPwGAEOKgEEIRQqgAHkGGlreJEEIciPx7CMCfI/M4qC8bI/8eysXcInwVwLtCiINA/ly3CE7XKS/uPyL6FoDZAK6OGAtEQirtka+3Qourj8nmvOK8hzm/bqT1Gf8/AP6oj+XimtnZDKTxfitm4/8OgLOI6IyI5/h1AGtyMZFI/PB3AP4uhHjAMG6Myf0rgO2xv5uFuVUQUX/9a2hJwu3QrtU3Iy/7JoDV2Z6bAZMXlg/XzYDTdVoD4NpIFcZ5ADoMy/WsQEQXAVgIYI4Q4qRhfAgRyZGvRwM4C8AHWZ6b03u4BsDXichPRGdE5vZ2NucG4EsAdgoh9usD2b5mTjYD6bzfspW9zsV/0DLgu6A9pe/I4TymQluebQPQFPnvYgB/APB+ZHwNgNNzMLfR0KormgG06NcJQDWAVwDsBvAygFNydO0qALQDqDKM5eS6QXsAfQwgBC2m+m2n6wSt6uKhyL33PoDGHMytFVocWL/nHo689rLIe90E4F0Al+Rgbo7vIYA7ItftHwC+ms15RcZ/D+DfY16b7WvmZDPSdr+xvAPDMEwJUsxhH4ZhGMYBNv4MwzAlCBt/hmGYEoSNP8MwTAnCxp9hGKYEYePPMC6JqDwOzvU8GCYdsPFnGIYpQdj4MyUHEV0bERRrJqI/ENElRLQ5Itr1MhGdGnldNRG9GNFTfxTaRhr9GNcQ0dsRbff/Nuz+7CSin0SO/ZbhWKeSpqnfHPnvi/GOwzCZho0/U1IQUQOAHwGYIYQYD+AmABsBnCc00a5noEkiAMCdADYKIRqgaR6NiBzjMwCuADBFCDEBgALg6sjvVAB4K3Ls1wF8JzL+awB/jYxPBNCS4DgMk1E8uZ4Aw2SZGQBWCiGOAIAQ4lMi+iyAP0b0ZnwAPoy8dho0gS8IIdYT0dHI+EwAnwPwjibBgnL0CmwFAayLfL0VwJcN5702ciwFQAcRfSPOcRgmo7DxZxjgQQAPCCHWENEF0LpMxYMA/I8Q4nabn4VEr2aKgvifsXjHYZiMwmEfptR4FcDlRFQNaD1RAVShV/72m4bXvg7gqsjrvgpAbzjyCoC5RDRUPwYRjUxw3lcAfC/yepmIqlI8DsOkBTb+TEkhhGgB8BMAfyWiZgAPQPP0VxLRVgBHDC+/G8A0ImqBFv7ZGznGDmh5gxeJaBuAl6D1XI3HTQCmE9H70MJB9Skeh2HSAqt6MgzDlCDs+TMMw5QgbPwZhmFKEDb+DMMwJQgbf4ZhmBKEjT/DMEwJwsafYRimBGHjzzAMU4L8/4LABxPmbYcxAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2025.1/_sources/samples/dbscan.ipynb.txt b/2025.1/_sources/samples/dbscan.ipynb.txt
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2025.1/_sources/samples/dbscan.ipynb.txt
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/kmeans.ipynb.txt b/2025.1/_sources/samples/kmeans.ipynb.txt
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2025.1/_sources/samples/kmeans.ipynb.txt
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/knn_mnist.ipynb.txt b/2025.1/_sources/samples/knn_mnist.ipynb.txt
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2025.1/_sources/samples/knn_mnist.ipynb.txt
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/lasso_regression.ipynb.txt b/2025.1/_sources/samples/lasso_regression.ipynb.txt
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2025.1/_sources/samples/lasso_regression.ipynb.txt
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/linear_regression.ipynb.txt b/2025.1/_sources/samples/linear_regression.ipynb.txt
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2025.1/_sources/samples/linear_regression.ipynb.txt
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/logistictic_regression_cifar.ipynb.txt b/2025.1/_sources/samples/logistictic_regression_cifar.ipynb.txt
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2025.1/_sources/samples/logistictic_regression_cifar.ipynb.txt
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/nusvr_medical_charges.ipynb.txt b/2025.1/_sources/samples/nusvr_medical_charges.ipynb.txt
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2025.1/_sources/samples/nusvr_medical_charges.ipynb.txt
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/random_forest_yolanda.ipynb.txt b/2025.1/_sources/samples/random_forest_yolanda.ipynb.txt
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2025.1/_sources/samples/random_forest_yolanda.ipynb.txt
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/ridge_regression.ipynb.txt b/2025.1/_sources/samples/ridge_regression.ipynb.txt
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2025.1/_sources/samples/ridge_regression.ipynb.txt
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/svc_adult.ipynb.txt b/2025.1/_sources/samples/svc_adult.ipynb.txt
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2025.1/_sources/samples/svc_adult.ipynb.txt
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/samples/tsne.ipynb.txt b/2025.1/_sources/samples/tsne.ipynb.txt
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2025.1/_sources/samples/tsne.ipynb.txt
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/_sources/support.rst.txt b/2025.1/_sources/support.rst.txt
new file mode 100644
index 0000000000..03a2dddadb
--- /dev/null
+++ b/2025.1/_sources/support.rst.txt
@@ -0,0 +1,34 @@
+.. ******************************************************************************
+.. * Copyright 2021 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+######################################################
+Intel(R) Extension for Scikit-learn Support
+######################################################
+
+
+We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn. 
+
+Use the following methods if you face any challenges. 
+
+
+Issues
+----------------------------------
+
+If you have a problem, check out the `GitHub Issues <https://github.com/intel/scikit-learn-intelex/issues>`_ to see if the issue you want to address is already reported. 
+
+You may find users that have encountered the same bug or have similar ideas for changes or updates.
+
+You can use issues to report a problem, make a feature request, or add comments on an existing issue.
diff --git a/2025.1/_sources/tutorials.rst.txt b/2025.1/_sources/tutorials.rst.txt
new file mode 100644
index 0000000000..ce778bb5ab
--- /dev/null
+++ b/2025.1/_sources/tutorials.rst.txt
@@ -0,0 +1,92 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+
+################################################
+|intelex| Tutorials & Case Studies
+################################################
+
+Explore the tutorials & case studies to help you to harness the full potential of |intelex|.
+
+Tutorials
+----------------------------------
+
+.. grid:: 3
+
+   .. grid-item-card:: Introduction to scikit-learn* Essentials for Machine Learning
+      :link: https://www.intel.com/content/www/us/en/developer/videos/introduction-to-scikit-learn-essentials-for-ml.html
+      :padding: 1
+
+      An overview of scikit-learn essentials.
+
+   .. grid-item-card:: Advanced scikit-learn* Essentials for Machine Learning
+      :link: https://www.intel.com/content/www/us/en/developer/videos/advanced-scikit-learn-essentials-for-ml.html
+      :padding: 1
+      
+      Special technique to perform scikit-learn computation on Intel GPUs. 
+   
+   .. grid-item-card:: Develop Efficient AI Solutions with Accelerated Machine Learning
+      :link: https://www.intel.com/content/www/us/en/developer/videos/accelerated-machine-learning-for-ai-solutions.html
+      :padding: 1
+
+      Techniques for maximizing Intel® Extension for Scikit-learn*. 
+
+   .. grid-item-card:: Getting started with classical Machine Learning Frameworks using Google Colaboratory
+      :link: https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Getting-started-with-classical-Machine-Learning-Frameworks-using/post/1450139
+      :padding: 1
+
+      Simple Installation of Intel® Extension for Scikit-learn* on Google Colaboratory.     
+
+   .. grid-item-card:: Accelerate Machine Learning Workloads: K-means and GPairs Algorithms
+      :link: https://www.intel.com/content/www/us/en/developer/videos/accelerate-ml-workloads-k-means-gpairs-algorithms.html
+      :padding: 1
+
+      An introduction to Intel® Extension for Scikit-learn*.
+
+   .. grid-item-card:: Benchmarking Intel® Extension for Scikit-learn*: How Much Faster Is It?
+      :link: https://www.intel.com/content/www/us/en/developer/articles/technical/benchmarking-intel-extension-for-scikit-learn.html
+      :padding: 1
+
+      Explore and compare the performance of the Intel Extension for Scikit-learn* vs. stock Scikit-learn*.
+
+   .. grid-item-card:: Drive Innovation & Performance into Your scikit-learn* Machine Learning Tasks
+      :link: https://www.intel.com/content/www/us/en/developer/videos/drive-innovation-performance-scikit-learn-ml-tasks.html
+      :padding: 1
+
+      An overview of scikit-learn essentials.
+
+Case Studies 
+----------------------------------
+
+.. grid:: 3
+
+   .. grid-item-card:: Greener Machine Learning Computing with Intel® AI Acceleration
+      :link: https://www.intel.com/content/www/us/en/developer/articles/case-study/greener-machine-learning-with-intel-ai.html
+      :padding: 1
+
+      Lowering the Environmental Impact of AI Workloads by 7x using Intel® Extension for Scikit-learn*.
+
+   .. grid-item-card:: Supply Chain Optimization at Enterprise Scale
+      :link: https://www.intel.com/content/www/us/en/developer/articles/technical/supply-chain-optimization-at-enterprise-scale.html
+      :padding: 1
+
+      Leveraging Open-Source AI Technologies with Red Hat OpenShift* Data Science and Intel® Architecture.
+
+   .. grid-item-card:: HippoScreen Improves AI Performance by 2.4x with oneAPI Tools
+      :link: https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html
+      :padding: 1
+
+      The neurotechnology startup used |intelex| to the improve efficiency and build times of deep-learning models used in its Brain Waves AI system.
\ No newline at end of file
diff --git a/2025.1/_sources/verbose.rst.txt b/2025.1/_sources/verbose.rst.txt
new file mode 100644
index 0000000000..cf77cc0669
--- /dev/null
+++ b/2025.1/_sources/verbose.rst.txt
@@ -0,0 +1,54 @@
+.. ******************************************************************************
+.. * Copyright 2020 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _verbose:
+
+############
+Verbose Mode
+############
+
+Use |intelex| in verbose mode to find out which implementation of the algorithm is currently used,
+|intelex| or original Scikit-learn.
+
+.. note:: Verbose mode is only available for :ref:`supported algorithms <sklearn_algorithms>`.
+
+To enable verbose mode, set the ``SKLEARNEX_VERBOSE`` environment variable as shown below:
+
+- On Linux* OS ::
+
+     export SKLEARNEX_VERBOSE=INFO
+
+- On Windows* OS ::
+
+     set SKLEARNEX_VERBOSE=INFO
+
+Alternatively, get |intelex| logger and set its logging level in the Python code::
+
+     import logging
+     logger = logging.getLogger('sklearnex')
+     logger.setLevel(logging.INFO)
+
+During the calls that use Intel-optimized scikit-learn, you will receive additional print statements
+that indicate which implementation is being called.
+These print statements are only available for :ref:`supported algorithms <sklearn_algorithms>`.
+
+For example, for DBSCAN you get one of these print statements depending on which implementation is used::
+
+    INFO:sklearnex: sklearn.cluster.DBSCAN.fit: running accelerated version on CPU
+
+::
+
+    INFO:sklearnex: sklearn.cluster.DBSCAN.fit: fallback to original Scikit-learn
diff --git a/2025.1/_sphinx_design_static/design-tabs.js b/2025.1/_sphinx_design_static/design-tabs.js
new file mode 100644
index 0000000000..b25bd6a4fa
--- /dev/null
+++ b/2025.1/_sphinx_design_static/design-tabs.js
@@ -0,0 +1,101 @@
+// @ts-check
+
+// Extra JS capability for selected tabs to be synced
+// The selection is stored in local storage so that it persists across page loads.
+
+/**
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+ */
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+const storageKeyPrefix = "sphinx-design-tab-id-";
+
+/**
+ * Create a key for a tab element.
+ * @param {HTMLElement} el - The tab element.
+ * @returns {[string, string, string] | null} - The key.
+ *
+ */
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+  let syncGroup = el.getAttribute("data-sync-group");
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+}
+
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+ *
+ */
+function ready() {
+  // Find all tabs with sync data
+
+  /** @type {string[]} */
+  let groups = [];
+
+  document.querySelectorAll(".sd-tab-label").forEach((label) => {
+    if (label instanceof HTMLElement) {
+      let data = create_key(label);
+      if (data) {
+        let [group, id, key] = data;
+
+        // add click event listener
+        // @ts-ignore
+        label.onclick = onSDLabelClick;
+
+        // store map of key to elements
+        if (!sd_id_to_elements[key]) {
+          sd_id_to_elements[key] = [];
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+        sd_id_to_elements[key].push(label);
+
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+          // Check if a specific tab has been selected via URL parameter
+          const tabParam = new URLSearchParams(window.location.search).get(
+            group
+          );
+          if (tabParam) {
+            console.log(
+              "sphinx-design: Selecting tab id for group '" +
+                group +
+                "' from URL parameter: " +
+                tabParam
+            );
+            window.sessionStorage.setItem(storageKeyPrefix + group, tabParam);
+          }
+        }
+
+        // Check is a specific tab has been selected previously
+        let previousId = window.sessionStorage.getItem(
+          storageKeyPrefix + group
+        );
+        if (previousId === id) {
+          // console.log(
+          //   "sphinx-design: Selecting tab from session storage: " + id
+          // );
+          // @ts-ignore
+          label.previousElementSibling.checked = true;
+        }
+      }
+    }
+  });
+}
+
+/**
+ *  Activate other tabs with the same sync id.
+ *
+ * @this {HTMLElement} - The element that was clicked.
+ */
+function onSDLabelClick() {
+  let data = create_key(this);
+  if (!data) return;
+  let [group, id, key] = data;
+  for (const label of sd_id_to_elements[key]) {
+    if (label === this) continue;
+    // @ts-ignore
+    label.previousElementSibling.checked = true;
+  }
+  window.sessionStorage.setItem(storageKeyPrefix + group, id);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/2025.1/_sphinx_design_static/sphinx-design.min.css b/2025.1/_sphinx_design_static/sphinx-design.min.css
new file mode 100644
index 0000000000..860c36da0f
--- /dev/null
+++ b/2025.1/_sphinx_design_static/sphinx-design.min.css
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#d3d4d5;--sd-color-muted-highlight: #5c636a;--sd-color-dark-highlight: #1c1f23;--sd-color-black-highlight: black;--sd-color-white-highlight: #d9d9d9;--sd-color-primary-bg: rgba(0, 113, 188, 0.2);--sd-color-secondary-bg: rgba(108, 117, 125, 0.2);--sd-color-success-bg: rgba(40, 167, 69, 0.2);--sd-color-info-bg: rgba(23, 162, 184, 0.2);--sd-color-warning-bg: rgba(240, 179, 126, 0.2);--sd-color-danger-bg: rgba(220, 53, 69, 0.2);--sd-color-light-bg: rgba(248, 249, 250, 0.2);--sd-color-muted-bg: rgba(108, 117, 125, 0.2);--sd-color-dark-bg: rgba(33, 37, 41, 0.2);--sd-color-black-bg: rgba(0, 0, 0, 0.2);--sd-color-white-bg: rgba(255, 255, 255, 0.2);--sd-color-primary-text: #fff;--sd-color-secondary-text: #fff;--sd-color-success-text: #fff;--sd-color-info-text: #fff;--sd-color-warning-text: #212529;--sd-color-danger-text: #fff;--sd-color-light-text: #212529;--sd-color-muted-text: #fff;--sd-color-dark-text: #fff;--sd-color-black-text: #fff;--sd-color-white-text: #212529;--sd-color-shadow: rgba(0, 0, 0, 0.15);--sd-color-card-border: rgba(0, 0, 0, 0.125);--sd-color-card-border-hover: hsla(231, 99%, 66%, 1);--sd-color-card-background: transparent;--sd-color-card-text: inherit;--sd-color-card-header: transparent;--sd-color-card-footer: transparent;--sd-color-tabs-label-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-hover: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-inactive: hsl(0, 0%, 66%);--sd-color-tabs-underline-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-underline-hover: rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem;--sd-fontsize-dropdown: inherit;--sd-fontsize-dropdown-title: 1rem;--sd-fontweight-dropdown-title: 700}
diff --git a/2025.1/_static/_sphinx_javascript_frameworks_compat.js b/2025.1/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000000..81415803ec
--- /dev/null
+++ b/2025.1/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/2025.1/_static/basic.css b/2025.1/_static/basic.css
new file mode 100644
index 0000000000..7ebbd6d07b
--- /dev/null
+++ b/2025.1/_static/basic.css
@@ -0,0 +1,914 @@
+/*
+ * Sphinx stylesheet -- basic theme.
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin-top: 10px;
+}
+
+ul.search li {
+    padding: 5px 0;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a:visited {
+    color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/2025.1/_static/check-solid.svg b/2025.1/_static/check-solid.svg
new file mode 100644
index 0000000000..92fad4b5c0
--- /dev/null
+++ b/2025.1/_static/check-solid.svg
@@ -0,0 +1,4 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>
diff --git a/2025.1/_static/clipboard.min.js b/2025.1/_static/clipboard.min.js
new file mode 100644
index 0000000000..54b3c46381
--- /dev/null
+++ b/2025.1/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},i="";return"string"==typeof t?(e=t,n="rtl"===document.documentElement.getAttribute("dir"),(o=document.createElement("textarea")).style.fontSize="12pt",o.style.border="0",o.style.padding="0",o.style.margin="0",o.style.position="absolute",o.style[n?"right":"left"]="-9999px",n=window.pageYOffset||document.documentElement.scrollTop,o.style.top="".concat(n,"px"),o.setAttribute("readonly",""),o.value=e,o=o,r.container.appendChild(o),i=c()(o),a("copy"),o.remove()):(i=c()(t),a("copy")),i};function r(t){return(r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==r(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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t=o?(t=m(this).constructor,Reflect.construct(e,arguments,t)):e.apply(this,arguments),e=this,!(t=t)||"object"!==d(t)&&"function"!=typeof t?function(t){if(void 0!==t)return t;throw new ReferenceError("this hasn't been initialised - super() hasn't been called")}(e):t}}function m(t){return(m=Object.setPrototypeOf?Object.getPrototypeOf:function(t){return t.__proto__||Object.getPrototypeOf(t)})(t)}function v(t,e){t="data-clipboard-".concat(t);if(e.hasAttribute(t))return e.getAttribute(t)}var o=function(){!function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),e&&y(t,e)}(r,i());var t,e,n,o=h(r);function r(t,e){var n;return function(t){if(!(t instanceof r))throw new TypeError("Cannot call a class as a function")}(this),(n=o.call(this)).resolveOptions(e),n.listenClick(t),n}return t=r,n=[{key:"copy",value:function(t){var 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e=t.delegateTarget||t.currentTarget,t=s({action:this.action(e),container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:this.action,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&p(t.prototype,e),n&&p(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function 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e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/2025.1/_static/copy-button.svg b/2025.1/_static/copy-button.svg
new file mode 100644
index 0000000000..9c074dae52
--- /dev/null
+++ b/2025.1/_static/copy-button.svg
@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>
diff --git a/2025.1/_static/copybutton.css b/2025.1/_static/copybutton.css
new file mode 100644
index 0000000000..f1916ec7d1
--- /dev/null
+++ b/2025.1/_static/copybutton.css
@@ -0,0 +1,94 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .3em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+    color: #22863a;
+}
+
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/2025.1/_static/copybutton.js b/2025.1/_static/copybutton.js
new file mode 100644
index 0000000000..2ea7ff3e21
--- /dev/null
+++ b/2025.1/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_success']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_to_clipboard']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>`
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      ${iconCopy}
+    </button>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/2025.1/_static/copybutton_funcs.js b/2025.1/_static/copybutton_funcs.js
new file mode 100644
index 0000000000..dbe1aaad79
--- /dev/null
+++ b/2025.1/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/2025.1/_static/css/badge_only.css b/2025.1/_static/css/badge_only.css
new file mode 100644
index 0000000000..88ba55b965
--- /dev/null
+++ b/2025.1/_static/css/badge_only.css
@@ -0,0 +1 @@
+.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions .rst-other-versions .rtd-current-item{font-weight:700}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}#flyout-search-form{padding:6px}
\ No newline at end of file
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diff --git a/2025.1/_static/css/fonts/Roboto-Slab-Regular.woff b/2025.1/_static/css/fonts/Roboto-Slab-Regular.woff
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diff --git a/2025.1/_static/css/fonts/fontawesome-webfont.eot b/2025.1/_static/css/fonts/fontawesome-webfont.eot
new file mode 100644
index 0000000000..e9f60ca953
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diff --git a/2025.1/_static/css/fonts/fontawesome-webfont.svg b/2025.1/_static/css/fonts/fontawesome-webfont.svg
new file mode 100644
index 0000000000..855c845e53
--- /dev/null
+++ b/2025.1/_static/css/fonts/fontawesome-webfont.svg
@@ -0,0 +1,2671 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg>
+<metadata>
+Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016
+ By ,,,
+Copyright Dave Gandy 2016. All rights reserved.
+</metadata>
+<defs>
+<font id="FontAwesome" horiz-adv-x="1536" >
+  <font-face 
+    font-family="FontAwesome"
+    font-weight="400"
+    font-stretch="normal"
+    units-per-em="1792"
+    panose-1="0 0 0 0 0 0 0 0 0 0"
+    ascent="1536"
+    descent="-256"
+    bbox="-1.02083 -256.962 2304.6 1537.02"
+    underline-thickness="0"
+    underline-position="0"
+    unicode-range="U+0020-F500"
+  />
+<missing-glyph horiz-adv-x="896" 
+d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
+    <glyph glyph-name=".notdef" horiz-adv-x="896" 
+d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
+    <glyph glyph-name=".null" horiz-adv-x="0" 
+ />
+    <glyph glyph-name="nonmarkingreturn" horiz-adv-x="597" 
+ />
+    <glyph glyph-name="space" unicode=" " horiz-adv-x="448" 
+ />
+    <glyph glyph-name="dieresis" unicode="&#xa8;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="copyright" unicode="&#xa9;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="registered" unicode="&#xae;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="acute" unicode="&#xb4;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="AE" unicode="&#xc6;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="Oslash" unicode="&#xd8;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="trademark" unicode="&#x2122;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="infinity" unicode="&#x221e;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="notequal" unicode="&#x2260;" horiz-adv-x="1792" 
+ />
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dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt:first-child{margin-top:0}html.writer-html4 .rst-content dl:not(.docutils) code.descclassname,html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descclassname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{background-color:transparent;border:none;padding:0;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .optional,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .optional{display:inline-block;padding:0 4px;color:#000;font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .property,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) 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diff --git a/2025.1/_static/custom.css b/2025.1/_static/custom.css
new file mode 100644
index 0000000000..1db55bd294
--- /dev/null
+++ b/2025.1/_static/custom.css
@@ -0,0 +1,131 @@
+/* override table width restrictions */
+@media screen and (min-width: 767px) {
+
+   .wy-table-responsive table td {
+      /* !important prevents the common CSS stylesheets from overriding
+         this as on RTD they are loaded after this stylesheet */
+
+      white-space: normal !important;
+   }
+}
+
+.code-block-caption {
+    color: #000;
+    font: italic 85%/1 arial,sans-serif;
+    padding: 1em 0;
+    text-align: center;
+}
+
+
+.collapsible {
+  margin-left: -10px;
+  background-color: #f1f1f1;
+  cursor: pointer;
+  padding: 18px 18px 18px 10px;
+  width: 100%;
+  border: none;
+  text-align: left;
+  outline: none;
+  font-weight: 700;
+  font-family: "Roboto Slab","ff-tisa-web-pro","Georgia",Arial,sans-serif;
+}
+
+code.sig-name.descname {
+   white-space: initial;
+}
+
+table.optimization-notice td {
+   white-space: initial;
+}
+
+table.optimization-notice td p:last-child {
+   text-align: right;
+}
+
+img.with-border {
+   border:1px solid #021a40;
+}
+
+div.column {
+   float: left;
+   width: 50%;
+   padding: 10px;
+ }
+
+/* Clear floats after the columns */
+.column:after {
+ content: "";
+ display: table;
+ clear: both;
+}
+
+.comparison:after {
+   content: "";
+   display: table;
+   clear: both;
+  }
+
+div.admonition-container {
+ width: 49%;
+ padding: 0 3px 0 0;
+}
+
+/* Clear floats after the columns */
+.admonition-container:after {
+ content: "";
+ display: table;
+ clear: both;
+}
+
+div.quotation {
+  background-color: #fffff1;
+  padding: 1em 0 0 1em;
+}
+
+.rst-content div[class^='highlight'] pre {
+   white-space: pre-wrap;
+}
+
+/* A workaround for https://github.com/readthedocs/sphinx_rtd_theme/issues/647
+ * Override display for function signatures so that there is spacing between
+ * types and arguments */
+ .rst-content dl:not(.docutils) dt {
+   display: table-cell !important;
+}
+.rst-content dl:not(.docutils) dd {
+   margin-top: 6px;
+}
+
+/*
+.rst-content tt.literal, .rst-content code.literal, .highlight {
+   background: #f0f0f0;
+}
+.rst-content tt.literal, .rst-content code.literal {
+    color: #000000;
+}*/
+
+
+.eqno {
+   margin-left: 5px;
+   float: right;
+}
+.math .headerlink {
+   display: none;
+   visibility: hidden;
+}
+.math:hover .headerlink {
+   display: inline-block;
+   visibility: visible;
+   margin-right: -0.7em;
+}
+
+a#wap_dns {display: none;}
+
+
+button#version-switcher-button {
+   background-color:  #2980b9;
+}
+
+div#version-switcher-dropdown {
+   display: inline;
+}
diff --git a/2025.1/_static/design-tabs.js b/2025.1/_static/design-tabs.js
new file mode 100644
index 0000000000..b25bd6a4fa
--- /dev/null
+++ b/2025.1/_static/design-tabs.js
@@ -0,0 +1,101 @@
+// @ts-check
+
+// Extra JS capability for selected tabs to be synced
+// The selection is stored in local storage so that it persists across page loads.
+
+/**
+ * @type {Record<string, HTMLElement[]>}
+ */
+let sd_id_to_elements = {};
+const storageKeyPrefix = "sphinx-design-tab-id-";
+
+/**
+ * Create a key for a tab element.
+ * @param {HTMLElement} el - The tab element.
+ * @returns {[string, string, string] | null} - The key.
+ *
+ */
+function create_key(el) {
+  let syncId = el.getAttribute("data-sync-id");
+  let syncGroup = el.getAttribute("data-sync-group");
+  if (!syncId || !syncGroup) return null;
+  return [syncGroup, syncId, syncGroup + "--" + syncId];
+}
+
+/**
+ * Initialize the tab selection.
+ *
+ */
+function ready() {
+  // Find all tabs with sync data
+
+  /** @type {string[]} */
+  let groups = [];
+
+  document.querySelectorAll(".sd-tab-label").forEach((label) => {
+    if (label instanceof HTMLElement) {
+      let data = create_key(label);
+      if (data) {
+        let [group, id, key] = data;
+
+        // add click event listener
+        // @ts-ignore
+        label.onclick = onSDLabelClick;
+
+        // store map of key to elements
+        if (!sd_id_to_elements[key]) {
+          sd_id_to_elements[key] = [];
+        }
+        sd_id_to_elements[key].push(label);
+
+        if (groups.indexOf(group) === -1) {
+          groups.push(group);
+          // Check if a specific tab has been selected via URL parameter
+          const tabParam = new URLSearchParams(window.location.search).get(
+            group
+          );
+          if (tabParam) {
+            console.log(
+              "sphinx-design: Selecting tab id for group '" +
+                group +
+                "' from URL parameter: " +
+                tabParam
+            );
+            window.sessionStorage.setItem(storageKeyPrefix + group, tabParam);
+          }
+        }
+
+        // Check is a specific tab has been selected previously
+        let previousId = window.sessionStorage.getItem(
+          storageKeyPrefix + group
+        );
+        if (previousId === id) {
+          // console.log(
+          //   "sphinx-design: Selecting tab from session storage: " + id
+          // );
+          // @ts-ignore
+          label.previousElementSibling.checked = true;
+        }
+      }
+    }
+  });
+}
+
+/**
+ *  Activate other tabs with the same sync id.
+ *
+ * @this {HTMLElement} - The element that was clicked.
+ */
+function onSDLabelClick() {
+  let data = create_key(this);
+  if (!data) return;
+  let [group, id, key] = data;
+  for (const label of sd_id_to_elements[key]) {
+    if (label === this) continue;
+    // @ts-ignore
+    label.previousElementSibling.checked = true;
+  }
+  window.sessionStorage.setItem(storageKeyPrefix + group, id);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/2025.1/_static/doctools.js b/2025.1/_static/doctools.js
new file mode 100644
index 0000000000..0398ebb9f0
--- /dev/null
+++ b/2025.1/_static/doctools.js
@@ -0,0 +1,149 @@
+/*
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/2025.1/_static/documentation_options.js b/2025.1/_static/documentation_options.js
new file mode 100644
index 0000000000..a70a1c083b
--- /dev/null
+++ b/2025.1/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '2025.1',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
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+++ b/2025.1/_static/js/badge_only.js
@@ -0,0 +1 @@
+!function(e){var t={};function r(n){if(t[n])return t[n].exports;var o=t[n]={i:n,l:!1,exports:{}};return e[n].call(o.exports,o,o.exports,r),o.l=!0,o.exports}r.m=e,r.c=t,r.d=function(e,t,n){r.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},r.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(r.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)r.d(n,o,function(t){return e[t]}.bind(null,o));return n},r.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return r.d(t,"a",t),t},r.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},r.p="",r(r.s=4)}({4:function(e,t,r){}});
\ No newline at end of file
diff --git a/2025.1/_static/js/theme.js b/2025.1/_static/js/theme.js
new file mode 100644
index 0000000000..1fddb6ee4a
--- /dev/null
+++ b/2025.1/_static/js/theme.js
@@ -0,0 +1 @@
+!function(n){var e={};function t(i){if(e[i])return e[i].exports;var o=e[i]={i:i,l:!1,exports:{}};return n[i].call(o.exports,o,o.exports,t),o.l=!0,o.exports}t.m=n,t.c=e,t.d=function(n,e,i){t.o(n,e)||Object.defineProperty(n,e,{enumerable:!0,get:i})},t.r=function(n){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(n,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(n,"__esModule",{value:!0})},t.t=function(n,e){if(1&e&&(n=t(n)),8&e)return n;if(4&e&&"object"==typeof n&&n&&n.__esModule)return n;var i=Object.create(null);if(t.r(i),Object.defineProperty(i,"default",{enumerable:!0,value:n}),2&e&&"string"!=typeof n)for(var o in n)t.d(i,o,function(e){return n[e]}.bind(null,o));return i},t.n=function(n){var e=n&&n.__esModule?function(){return n.default}:function(){return n};return t.d(e,"a",e),e},t.o=function(n,e){return Object.prototype.hasOwnProperty.call(n,e)},t.p="",t(t.s=0)}([function(n,e,t){t(1),n.exports=t(3)},function(n,e,t){(function(){var e="undefined"!=typeof window?window.jQuery:t(2);n.exports.ThemeNav={navBar:null,win:null,winScroll:!1,winResize:!1,linkScroll:!1,winPosition:0,winHeight:null,docHeight:null,isRunning:!1,enable:function(n){var t=this;void 0===n&&(n=!0),t.isRunning||(t.isRunning=!0,e((function(e){t.init(e),t.reset(),t.win.on("hashchange",t.reset),n&&t.win.on("scroll",(function(){t.linkScroll||t.winScroll||(t.winScroll=!0,requestAnimationFrame((function(){t.onScroll()})))})),t.win.on("resize",(function(){t.winResize||(t.winResize=!0,requestAnimationFrame((function(){t.onResize()})))})),t.onResize()})))},enableSticky:function(){this.enable(!0)},init:function(n){n(document);var e=this;this.navBar=n("div.wy-side-scroll:first"),this.win=n(window),n(document).on("click","[data-toggle='wy-nav-top']",(function(){n("[data-toggle='wy-nav-shift']").toggleClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift")})).on("click",".wy-menu-vertical .current ul li a",(function(){var t=n(this);n("[data-toggle='wy-nav-shift']").removeClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift"),e.toggleCurrent(t),e.hashChange()})).on("click","[data-toggle='rst-current-version']",(function(){n("[data-toggle='rst-versions']").toggleClass("shift-up")})),n("table.docutils:not(.field-list,.footnote,.citation)").wrap("<div class='wy-table-responsive'></div>"),n("table.docutils.footnote").wrap("<div class='wy-table-responsive footnote'></div>"),n("table.docutils.citation").wrap("<div class='wy-table-responsive citation'></div>"),n(".wy-menu-vertical ul").not(".simple").siblings("a").each((function(){var t=n(this);expand=n('<button class="toctree-expand" title="Open/close menu"></button>'),expand.on("click",(function(n){return e.toggleCurrent(t),n.stopPropagation(),!1})),t.prepend(expand)}))},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),t=e.find('[href="'+n+'"]');if(0===t.length){var i=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(t=e.find('[href="#'+i.attr("id")+'"]')).length&&(t=e.find('[href="#"]'))}if(t.length>0){$(".wy-menu-vertical .current").removeClass("current").attr("aria-expanded","false"),t.addClass("current").attr("aria-expanded","true"),t.closest("li.toctree-l1").parent().addClass("current").attr("aria-expanded","true");for(let n=1;n<=10;n++)t.closest("li.toctree-l"+n).addClass("current").attr("aria-expanded","true");t[0].scrollIntoView()}}catch(n){console.log("Error expanding nav for anchor",n)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,t=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(t),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",(function(){this.linkScroll=!1}))},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current").attr("aria-expanded","false"),e.siblings().find("li.current").removeClass("current").attr("aria-expanded","false");var t=e.find("> ul li");t.length&&(t.removeClass("current").attr("aria-expanded","false"),e.toggleClass("current").attr("aria-expanded",(function(n,e){return"true"==e?"false":"true"})))}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t<e.length&&!window.requestAnimationFrame;++t)window.requestAnimationFrame=window[e[t]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[e[t]+"CancelAnimationFrame"]||window[e[t]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(e,t){var i=(new Date).getTime(),o=Math.max(0,16-(i-n)),r=window.setTimeout((function(){e(i+o)}),o);return n=i+o,r}),window.cancelAnimationFrame||(window.cancelAnimationFrame=function(n){clearTimeout(n)})}()}).call(window)},function(n,e){n.exports=jQuery},function(n,e,t){}]);
\ No newline at end of file
diff --git a/2025.1/_static/js/versions.js b/2025.1/_static/js/versions.js
new file mode 100644
index 0000000000..4958195e07
--- /dev/null
+++ b/2025.1/_static/js/versions.js
@@ -0,0 +1,228 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = true;
+const themeLanguageSelector = true;
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    // Insert the current language to the options on the selector
+    let languages = config.projects.translations.concat(config.projects.current);
+    languages = languages.sort((a, b) => a.language.name.localeCompare(b.language.name));
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${languages
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/2025.1/_static/language_data.js b/2025.1/_static/language_data.js
new file mode 100644
index 0000000000..c7fe6c6faf
--- /dev/null
+++ b/2025.1/_static/language_data.js
@@ -0,0 +1,192 @@
+/*
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/2025.1/_static/minus.png b/2025.1/_static/minus.png
new file mode 100644
index 0000000000..d96755fdaf
Binary files /dev/null and b/2025.1/_static/minus.png differ
diff --git a/2025.1/_static/nbsphinx-broken-thumbnail.svg b/2025.1/_static/nbsphinx-broken-thumbnail.svg
new file mode 100644
index 0000000000..4919ca8829
--- /dev/null
+++ b/2025.1/_static/nbsphinx-broken-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: lightcoral; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: crimson; }
+    }
+  </style>
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new file mode 100644
index 0000000000..a3fb27c30f
--- /dev/null
+++ b/2025.1/_static/nbsphinx-code-cells.css
@@ -0,0 +1,259 @@
+/* remove conflicting styling from Sphinx themes */
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+    background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
+    }
+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
+
+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
+}
+
+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+    color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+    color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+    width: 4.5ex;
+    padding-top: 5px;
+    position: relative;
+    user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+    position: absolute;
+    right: 0;
+    margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+    div.nbinput.container div.prompt,
+    div.nboutput.container div.prompt {
+        width: unset;
+        text-align: left;
+        padding: 0.4em;
+    }
+    div.nboutput.container div.prompt.empty {
+        padding: 0;
+    }
+
+    div.nbinput.container div.prompt > div,
+    div.nboutput.container div.prompt > div {
+        position: unset;
+    }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+    overflow: hidden;
+    white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+    -webkit-flex: 1;
+    flex: 1;
+    overflow: auto;
+}
+@media (max-width: 540px) {
+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
+        width: 100%;
+    }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+    border: 1px solid #e0e0e0;
+    border-radius: 2px;
+    /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
+    text-align: left !important;
+}
+
+/* override sphinx.ext.imgmath center alignment in output cells */
+div.nboutput.container div.math p {
+    text-align: left;
+}
+
+/* standard error */
+div.nboutput.container div.output_area.stderr {
+    background: #fdd;
+}
+
+/* ANSI colors */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
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+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
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+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
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+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
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+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+  font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
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new file mode 100644
index 0000000000..365c27a96b
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+.nbsphinx-gallery {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 5px;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+    padding: 5px;
+    border: 1px dotted currentColor;
+    border-radius: 2px;
+    text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+    border-style: solid;
+}
+
+.nbsphinx-gallery img {
+    max-width: 100%;
+    max-height: 100%;
+}
+
+.nbsphinx-gallery > a > div:first-child {
+    display: flex;
+    align-items: start;
+    justify-content: center;
+    height: 120px;
+    margin-bottom: 5px;
+}
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new file mode 100644
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+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #06287e } /* Name.Function.Magic */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
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diff --git a/2025.1/_static/searchtools.js b/2025.1/_static/searchtools.js
new file mode 100644
index 0000000000..2c774d17af
--- /dev/null
+++ b/2025.1/_static/searchtools.js
@@ -0,0 +1,632 @@
+/*
+ * Sphinx JavaScript utilities for the full-text search.
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename, kind] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+// Global search result kind enum, used by themes to style search results.
+class SearchResultKind {
+    static get index() { return  "index"; }
+    static get object() { return "object"; }
+    static get text() { return "text"; }
+    static get title() { return "title"; }
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename, kind] = item;
+
+  let listItem = document.createElement("li");
+  // Add a class representing the item's type:
+  // can be used by a theme's CSS selector for styling
+  // See SearchResultKind for the class names.
+  listItem.classList.add(`kind-${kind}`);
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = Documentation.ngettext(
+      "Search finished, found one page matching the search query.",
+      "Search finished, found ${resultCount} pages matching the search query.",
+      resultCount,
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlink", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.setAttribute("role", "list");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename, kind].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score + boost,
+            filenames[file],
+            SearchResultKind.title,
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+            SearchResultKind.index,
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+        SearchResultKind.object,
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+        SearchResultKind.text,
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/2025.1/_static/sphinx-design.min.css b/2025.1/_static/sphinx-design.min.css
new file mode 100644
index 0000000000..860c36da0f
--- /dev/null
+++ b/2025.1/_static/sphinx-design.min.css
@@ -0,0 +1 @@
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diff --git a/2025.1/_static/sphinx_highlight.js b/2025.1/_static/sphinx_highlight.js
new file mode 100644
index 0000000000..8a96c69a19
--- /dev/null
+++ b/2025.1/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/2025.1/_static/tabs.css b/2025.1/_static/tabs.css
new file mode 100644
index 0000000000..957ba60d69
--- /dev/null
+++ b/2025.1/_static/tabs.css
@@ -0,0 +1,89 @@
+.sphinx-tabs {
+  margin-bottom: 1rem;
+}
+
+[role="tablist"] {
+  border-bottom: 1px solid #a0b3bf;
+}
+
+.sphinx-tabs-tab {
+  position: relative;
+  font-family: Lato,'Helvetica Neue',Arial,Helvetica,sans-serif;
+  color: #1D5C87;
+  line-height: 24px;
+  margin: 0;
+  font-size: 16px;
+  font-weight: 400;
+  background-color: rgba(255, 255, 255, 0);
+  border-radius: 5px 5px 0 0;
+  border: 0;
+  padding: 1rem 1.5rem;
+  margin-bottom: 0;
+}
+
+.sphinx-tabs-tab[aria-selected="true"] {
+  font-weight: 700;
+  border: 1px solid #a0b3bf;
+  border-bottom: 1px solid white;
+  margin: -1px;
+  background-color: white;
+}
+
+.sphinx-tabs-tab:focus {
+  z-index: 1;
+  outline-offset: 1px;
+}
+
+.sphinx-tabs-panel {
+  position: relative;
+  padding: 1rem;
+  border: 1px solid #a0b3bf;
+  margin: 0px -1px -1px -1px;
+  border-radius: 0 0 5px 5px;
+  border-top: 0;
+  background: white;
+}
+
+.sphinx-tabs-panel.code-tab {
+  padding: 0.4rem;
+}
+
+.sphinx-tab img {
+	margin-bottom: 24 px;
+}
+
+/* Dark theme preference styling */
+
+@media (prefers-color-scheme: dark) {
+  body[data-theme="auto"] .sphinx-tabs-panel {
+    color: white;
+    background-color: rgb(50, 50, 50);
+  }
+
+  body[data-theme="auto"] .sphinx-tabs-tab {
+    color: white;
+    background-color: rgba(255, 255, 255, 0.05);
+  }
+
+  body[data-theme="auto"] .sphinx-tabs-tab[aria-selected="true"] {
+    border-bottom: 1px solid rgb(50, 50, 50);
+    background-color: rgb(50, 50, 50);
+  }
+}
+
+/* Explicit dark theme styling */
+
+body[data-theme="dark"] .sphinx-tabs-panel {
+  color: white;
+  background-color: rgb(50, 50, 50);
+}
+
+body[data-theme="dark"] .sphinx-tabs-tab {
+  color: white;
+  background-color: rgba(255, 255, 255, 0.05);
+}
+
+body[data-theme="dark"] .sphinx-tabs-tab[aria-selected="true"] {
+  border-bottom: 2px solid rgb(50, 50, 50);
+  background-color: rgb(50, 50, 50);
+}
diff --git a/2025.1/_static/tabs.js b/2025.1/_static/tabs.js
new file mode 100644
index 0000000000..48dc303c8c
--- /dev/null
+++ b/2025.1/_static/tabs.js
@@ -0,0 +1,145 @@
+try {
+  var session = window.sessionStorage || {};
+} catch (e) {
+  var session = {};
+}
+
+window.addEventListener("DOMContentLoaded", () => {
+  const allTabs = document.querySelectorAll('.sphinx-tabs-tab');
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+
+  allTabs.forEach(tab => {
+    tab.addEventListener("click", changeTabs);
+  });
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+  tabLists.forEach(tabList => {
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+  });
+
+  // Restore group tab selection from session
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+  if (lastSelected != null) selectNamedTabs(lastSelected);
+});
+
+/**
+ * Key focus left and right between sibling elements using arrows
+ * @param  {Node} e the element in focus when key was pressed
+ */
+function keyTabs(e) {
+    const tab = e.target;
+    let nextTab = null;
+    if (e.keyCode === 39 || e.keyCode === 37) {
+      tab.setAttribute("tabindex", -1);
+      // Move right
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+      // Move left
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+        if (nextTab === null) {
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+      }
+    }
+
+    if (nextTab !== null) {
+      nextTab.setAttribute("tabindex", 0);
+      nextTab.focus();
+    }
+}
+
+/**
+ * Select or deselect clicked tab. If a group tab
+ * is selected, also select tab in other tabLists.
+ * @param  {Node} e the element that was clicked
+ */
+function changeTabs(e) {
+  // Use this instead of the element that was clicked, in case it's a child
+  const notSelected = this.getAttribute("aria-selected") === "false";
+  const positionBefore = this.parentNode.getBoundingClientRect().top;
+  const notClosable = !this.parentNode.classList.contains("closeable");
+
+  deselectTabList(this);
+
+  if (notSelected || notClosable) {
+    selectTab(this);
+    const name = this.getAttribute("name");
+    selectNamedTabs(name, this.id);
+
+    if (this.classList.contains("group-tab")) {
+      // Persist during session
+      session.setItem('sphinx-tabs-last-selected', name);
+    }
+  }
+
+  const positionAfter = this.parentNode.getBoundingClientRect().top;
+  const positionDelta = positionAfter - positionBefore;
+  // Scroll to offset content resizing
+  window.scrollTo(0, window.scrollY + positionDelta);
+}
+
+/**
+ * Select tab and show associated panel.
+ * @param  {Node} tab tab to select
+ */
+function selectTab(tab) {
+  tab.setAttribute("aria-selected", true);
+
+  // Show the associated panel
+  document
+    .getElementById(tab.getAttribute("aria-controls"))
+    .removeAttribute("hidden");
+}
+
+/**
+ * Hide the panels associated with all tabs within the
+ * tablist containing this tab.
+ * @param  {Node} tab a tab within the tablist to deselect
+ */
+function deselectTabList(tab) {
+  const parent = tab.parentNode;
+  const grandparent = parent.parentNode;
+
+  Array.from(parent.children)
+  .forEach(t => t.setAttribute("aria-selected", false));
+
+  Array.from(grandparent.children)
+    .slice(1)  // Skip tablist
+    .forEach(panel => panel.setAttribute("hidden", true));
+}
+
+/**
+ * Select grouped tabs with the same name, but no the tab
+ * with the given id.
+ * @param  {Node} name name of grouped tab to be selected
+ * @param  {Node} clickedId id of clicked tab
+ */
+function selectNamedTabs(name, clickedId=null) {
+  const groupedTabs = document.querySelectorAll(`.sphinx-tabs-tab[name="${name}"]`);
+  const tabLists = Array.from(groupedTabs).map(tab => tab.parentNode);
+
+  tabLists
+    .forEach(tabList => {
+      // Don't want to change the tabList containing the clicked tab
+      const clickedTab = tabList.querySelector(`[id="${clickedId}"]`);
+      if (clickedTab === null ) {
+        // Select first tab with matching name
+        const tab = tabList.querySelector(`.sphinx-tabs-tab[name="${name}"]`);
+        deselectTabList(tab);
+        selectTab(tab);
+      }
+    })
+}
+
+if (typeof exports === 'undefined') {
+  exports = {};
+}
+
+exports.keyTabs = keyTabs;
+exports.changeTabs = changeTabs;
+exports.selectTab = selectTab;
+exports.deselectTabList = deselectTabList;
+exports.selectNamedTabs = selectNamedTabs;
diff --git a/2025.1/_static/version_switcher.js b/2025.1/_static/version_switcher.js
new file mode 100644
index 0000000000..162851d217
--- /dev/null
+++ b/2025.1/_static/version_switcher.js
@@ -0,0 +1,31 @@
+function load_versions(json){
+    var button = document.getElementById('version-switcher-button')
+    var container = document.getElementById('version-switcher-dropdown')
+    var loc = window.location.href;
+    var s = document.createElement('select');
+    s.style = "border-radius:5px;"
+    const versions = JSON.parse(json);
+    for (entry of versions){
+        var o = document.createElement('option');
+        var optionText = '';
+        if ('name' in entry){
+            optionText = entry.name;
+        }else{
+            optionText = entry.version;
+        }
+        o.value = entry.url;
+        if (current_version == entry.version){
+            o.selected = true;
+        }
+        o.innerHTML = optionText;
+        s.append(o);
+    }
+    s.addEventListener("change", ()=> {
+        var current_url = new URL(window.location.href);
+        var path = current_url.pathname;
+        //strip version from path
+        var page_path = path.substring(project_name.length+current_version.length+3);
+        window.location.href = s.value + page_path;
+    });
+    container.append(s);
+}
diff --git a/2025.1/algorithms.html b/2025.1/algorithms.html
new file mode 100644
index 0000000000..0a562f4680
--- /dev/null
+++ b/2025.1/algorithms.html
@@ -0,0 +1,1238 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Supported Algorithms &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+      <link rel="stylesheet" type="text/css" href="_static/custom.css?v=132153fd" />
+      <link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
+
+  
+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="_static/documentation_options.js?v=aa5d0537"></script>
+      <script src="_static/doctools.js?v=9bcbadda"></script>
+      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="_static/copybutton.js?v=f281be69"></script>
+      <script src="_static/version_switcher.js?v=107152b5"></script>
+      <script src="_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="oneAPI and GPU support in Intel® Extension for Scikit-learn*" href="oneapi-gpu.html" />
+    <link rel="prev" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" href="kaggle/automl.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
+          (function () {
+            var url = 'https://www.intel.com/content/dam/www/global/wap/tms-loader.js'; // WAP file URL
+            var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = url;
+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
+          })();
+      }
+      </script>
+
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
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+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li class="breadcrumb-item active">Supported Algorithms</li>
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+            <a href="_sources/algorithms.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="supported-algorithms">
+<span id="sklearn-algorithms"></span><h1>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Link to this heading"></a></h1>
+<p>Applying Intel® Extension for Scikit-learn* impacts the following scikit-learn algorithms:</p>
+<section id="on-cpu">
+<h2>on CPU<a class="headerlink" href="#on-cpu" title="Link to this heading"></a></h2>
+<section id="classification">
+<h3>Classification<a class="headerlink" href="#classification" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>SVC</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>NuSVC</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>RandomForestClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsClassifier</cite></p></td>
+<td><ul>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == <cite>‘kd_tree’</cite>:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p>
+</li>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == <cite>‘brute’</cite>:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p>
+</li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LogisticRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> not in [<cite>‘lbfgs’</cite>, <cite>‘newton-cg’</cite>]</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="regression">
+<h3>Regression<a class="headerlink" href="#regression" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>SVR</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>NuSVR</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>RandomForestRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LinearRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported.</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>Ridge</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> != <cite>‘auto’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>ElasticNet</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>Lasso</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="clustering">
+<h3>Clustering<a class="headerlink" href="#clustering" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>KMeans</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>No limitations</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>DBSCAN</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> not in [<cite>‘brute’</cite>, <cite>‘auto’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="dimensionality-reduction">
+<h3>Dimensionality Reduction<a class="headerlink" href="#dimensionality-reduction" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>PCA</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>IncrementalPCA</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>TSNE</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != ‘euclidean’ or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+<p>Refer to <a class="reference internal" href="guide/acceleration.html#acceleration-tsne"><span class="std std-ref">TSNE acceleration details</span></a> to learn more.</p>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="nearest-neighbors">
+<h3>Nearest Neighbors<a class="headerlink" href="#nearest-neighbors" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>NearestNeighbors</cite></p></td>
+<td><ul>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == ‘kd_tree’:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p>
+</li>
+<li><p>For <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> == ‘brute’:</p>
+<p>all parameters except <code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p>
+</li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="other-tasks">
+<h3>Other Tasks<a class="headerlink" href="#other-tasks" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>EmpiricalCovariance</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>train_test_split</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>assert_all_finite</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>pairwise_distance</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘cosine’</cite>, <cite>‘correlation’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>roc_auc_score</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">average</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">max_fpr</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">multi_class</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>No limitations</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="on-gpu">
+<h2>on GPU<a class="headerlink" href="#on-gpu" title="Link to this heading"></a></h2>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="oneapi-gpu.html#oneapi-gpu"><span class="std std-ref">oneAPI and GPU support in Intel® Extension for Scikit-learn*</span></a></p>
+</div>
+<section id="id1">
+<h3>Classification<a class="headerlink" href="#id1" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>SVC</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">kernel</span></code> = <cite>‘sigmoid_poly’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only binary dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>RandomForestClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>KNeighborsClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>LogisticRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> != <cite>‘newton-cg’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">penalty</span></code> != <cite>‘l2’</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id2">
+<h3>Regression<a class="headerlink" href="#id2" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>RandomForestRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LinearRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id3">
+<h3>Clustering<a class="headerlink" href="#id3" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>KMeans</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">Init</span></code> = <cite>‘k-means++’</cite> fallbacks to CPU.</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>DBSCAN</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> not in [<cite>‘brute’</cite>, <cite>‘auto’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id4">
+<h3>Dimensionality Reduction<a class="headerlink" href="#id4" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>PCA</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id5">
+<h3>Nearest Neighbors<a class="headerlink" href="#id5" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>NearestNeighbors</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id6">
+<h3>Other Tasks<a class="headerlink" href="#id6" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>EmpiricalCovariance</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="spmd-support">
+<h2>SPMD Support<a class="headerlink" href="#spmd-support" title="Link to this heading"></a></h2>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="distributed-mode.html#distributed"><span class="std std-ref">Distributed Mode</span></a></p>
+</div>
+<section id="id7">
+<h3>Classification<a class="headerlink" href="#id7" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>RandomForestClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> method not supported</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LogisticRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> != <cite>‘newton-cg’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">penalty</span></code> != <cite>‘l2’</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id8">
+<h3>Regression<a class="headerlink" href="#id8" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>RandomForestRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LinearRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id9">
+<h3>Clustering<a class="headerlink" href="#id9" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>KMeans</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">Init</span></code> = <cite>‘k-means++’</cite> fallbacks to CPU.</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>DBSCAN</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> not in [<cite>‘brute’</cite>, <cite>‘auto’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id10">
+<h3>Dimensionality Reduction<a class="headerlink" href="#id10" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>PCA</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fit</span></code> is the only method supported</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id11">
+<h3>Nearest Neighbors<a class="headerlink" href="#id11" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>NearestNeighbors</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id12">
+<h3>Other Tasks<a class="headerlink" href="#id12" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>EmpiricalCovariance</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="scikit-learn-tests">
+<h2>Scikit-learn Tests<a class="headerlink" href="#scikit-learn-tests" title="Link to this heading"></a></h2>
+<p>Monkey-patched scikit-learn classes and functions passes scikit-learn’s own test
+suite, with few exceptions, specified in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/deselected_tests.yaml">deselected_tests.yaml</a>.</p>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="kaggle/automl.html" class="btn btn-neutral float-left" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+      current_version = "2025.1"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
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+      <li class="breadcrumb-item active">Array API support</li>
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+            <a href="_sources/array_api.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="array-api-support">
+<span id="array-api"></span><h1>Array API support<a class="headerlink" href="#array-api-support" title="Link to this heading"></a></h1>
+<p>The <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API</a> specification defines
+a standard API for all array manipulation libraries with a NumPy-like API.
+Intel(R) Extension for Scikit-Learn doesn’t require
+<a class="reference external" href="https://github.com/data-apis/array-api-compat">array-api-compat</a> to be installed for
+functional support of the array API standard.
+In the current implementation, the functional support of array api follows the functional
+support of different array or DataFrame inputs and does not modify the precision of the
+input and output data formats unless necessary. Any array API input will be converted to host
+numpy.ndarrays and all internal manipulations with data will be done with these representations of
+the input data. DPNP’s ‘ndarray’ and Data Parallel Control’s ‘usm_ndarray’ have special handling
+requirements that are described in the relevant section of this document. Output values will in
+all relevant cases match the input data format.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Currently, only <a class="reference external" href="https://github.com/data-apis/array-api-strict">array-api-strict</a>,
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a>, <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp</a>
+and <a class="reference external" href="https://numpy.org/">numpy</a> are known to work with sklearnex estimators.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Stock Scikit-learn’s array API support requires <a class="reference external" href="https://github.com/data-apis/array-api-compat">array-api-compat</a> to be installed.</p>
+</div>
+<section id="support-for-dpnp-and-dpctl">
+<h2>Support for DPNP and DPCTL<a class="headerlink" href="#support-for-dpnp-and-dpctl" title="Link to this heading"></a></h2>
+<p>The functional support of input data for sklearnex estimators also extended for SYCL USM array types.
+These include SYCL USM arrays <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp’s</a> ndarray and
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">Data Parallel Control usm_ndarray</a>.
+DPNP ndarray and Data Parallel Control usm_ndarray contain SYCL contexts which can be used for
+<cite>sklearnex</cite> device offloading.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Current support for DPNP and DPCTL usm_ndarray data can be copied and moved to and from device in sklearnex and have
+impacts on memory utilization.</p>
+</div>
+<p>DPCTL or DPNP inputs are not required to use <cite>config_context(target_offload=device)</cite>.
+<cite>sklearnex</cite> will use input usm_ndarray sycl context for device offloading.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>As DPCTL or DPNP inputs contain SYCL contexts, they do not require <cite>config_context(target_offload=device)</cite>.
+However, the use of <cite>config_context`</cite> will override the contained SYCL context and will force movement
+of data to the targeted device.</p>
+</div>
+</section>
+<section id="support-for-array-api-compatible-inputs">
+<h2>Support for Array API-compatible inputs<a class="headerlink" href="#support-for-array-api-compatible-inputs" title="Link to this heading"></a></h2>
+<p>All patched estimators, metrics, tools and non-scikit-learn estimators functionally support Array API standard.
+Intel(R) Extension for scikit-Learn preserves input data format for all outputs. For all array inputs except
+SYCL USM arrays <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp’s</a> ndarray and
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">Data Parallel Control usm_ndarray</a> all computation
+will be only accomplished on CPU unless specified by a <cite>config_context`</cite> with an available GPU device.</p>
+<p>Stock scikit-learn uses <cite>config_context(array_api_dispatch=True)</cite> for enabling Array API
+<a class="reference external" href="https://scikit-learn.org/1.5/modules/array_api.html">support</a>.
+If <cite>array_api_dispatch</cite> is enabled and the installed Scikit-Learn version supports array API, then the original
+inputs are used when falling back to Scikit-Learn functionality.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Data Parallel Control usm_ndarray or DPNP ndarray inputs will use host numpy data copies when
+falling back to Scikit-Learn since they are not array API compliant.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Functional support doesn’t guarantee that after the model is trained, fitted attributes that are arrays
+will also be from the same namespace as the training data.</p>
+</div>
+</section>
+<section id="example-usage">
+<h2>Example usage<a class="headerlink" href="#example-usage" title="Link to this heading"></a></h2>
+<section id="dpnp-ndarrays">
+<h3>DPNP ndarrays<a class="headerlink" href="#dpnp-ndarrays" title="Link to this heading"></a></h3>
+<p>Here is an example code to demonstrate how to use <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp</a> arrays to
+run <cite>RandomForestRegressor</cite> on a GPU without <cite>config_context(array_api_dispatch=True)</cite>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2023 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="c1"># sklearnex RF example for GPU offloading with DPNP ndarray:</span>
+<span class="c1">#    python ./random_forest_regressor_dpnp_batch.py</span>
+
+<span class="kn">import</span> <span class="nn">dpnp</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+
+<span class="kn">from</span> <span class="nn">sklearnex.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
+
+<span class="n">sycl_device</span> <span class="o">=</span> <span class="s2">&quot;gpu:0&quot;</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="n">dpnp_X_train</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">sycl_device</span><span class="p">)</span>
+<span class="n">dpnp_y_train</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">sycl_device</span><span class="p">)</span>
+<span class="n">dpnp_X_test</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">sycl_device</span><span class="p">)</span>
+
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dpnp_X_train</span><span class="p">,</span> <span class="n">dpnp_y_train</span><span class="p">)</span>
+
+<span class="n">pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">dpnp_X_test</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Random Forest regression results:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Ground truth (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">y_test</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Regression results (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Are predicted results on GPU: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">sycl_device</span><span class="o">.</span><span class="n">is_gpu</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Functional support doesn’t guarantee that after the model is trained, fitted attributes that are arrays
+will also be from the same namespace as the training data.</p>
+</div>
+<p>For example, if <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp’s</a> namespace was used for training,
+then fitted attributes will be on the CPU and <cite>numpy.ndarray</cite> data format.</p>
+</section>
+<section id="dpctl-usm-ndarrays">
+<h3>DPCTL usm_ndarrays<a class="headerlink" href="#dpctl-usm-ndarrays" title="Link to this heading"></a></h3>
+<p>Here is an example code to demonstrate how to use <a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a>
+arrays to run <cite>RandomForestClassifier</cite> on a GPU witout <cite>config_context(array_api_dispatch=True)</cite>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2023 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="c1"># sklearnex RF example for GPU offloading with DPCtl tensor:</span>
+<span class="c1">#    python ./random_forest_classifier_dpctl_batch.py</span>
+
+<span class="kn">import</span> <span class="nn">dpctl</span>
+<span class="kn">import</span> <span class="nn">dpctl.tensor</span> <span class="k">as</span> <span class="nn">dpt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+
+<span class="kn">from</span> <span class="nn">sklearnex.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
+
+<span class="c1"># Make sure that all DPCtl tensors using the same device.</span>
+<span class="n">q</span> <span class="o">=</span> <span class="n">dpctl</span><span class="o">.</span><span class="n">SyclQueue</span><span class="p">(</span><span class="s2">&quot;gpu&quot;</span><span class="p">)</span>  <span class="c1"># GPU</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+    <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="n">dpt_X_train</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">usm_type</span><span class="o">=</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">sycl_queue</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+<span class="n">dpt_y_train</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">usm_type</span><span class="o">=</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">sycl_queue</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+<span class="n">dpt_X_test</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">usm_type</span><span class="o">=</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">sycl_queue</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dpt_X_train</span><span class="p">,</span> <span class="n">dpt_y_train</span><span class="p">)</span>
+
+<span class="n">pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">dpt_X_test</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Random Forest classification results:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Ground truth (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">y_test</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Classification results (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Are predicted results on GPU: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">sycl_device</span><span class="o">.</span><span class="n">is_gpu</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>As on previous example, if <a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> Array API namespace was
+used for training, then fitted attributes will be on the CPU and <cite>numpy.ndarray</cite> data format.</p>
+</section>
+<section id="use-of-array-api-strict">
+<h3>Use of <cite>array-api-strict</cite><a class="headerlink" href="#use-of-array-api-strict" title="Link to this heading"></a></h3>
+<p>Here is an example code to demonstrate how to use <a class="reference external" href="https://github.com/data-apis/array-api-strict">array-api-strict</a>
+arrays to run <cite>DBSCAN</cite>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">array_api_strict</span>
+
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">config_context</span><span class="p">,</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">array_api_strict</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
+    <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.0</span><span class="p">,</span> <span class="mf">80.0</span><span class="p">]],</span>
+    <span class="n">dtype</span><span class="o">=</span><span class="n">array_api_strict</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Could be launched without `config_context(array_api_dispatch=True)`. This context</span>
+<span class="c1"># manager for sklearnex, only guarantee that in case of the fallback to stock</span>
+<span class="c1"># scikit-learn, fitted attributes to be from the same Array API namespace as</span>
+<span class="c1"># the training data.</span>
+<span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Fitted labels :</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">clustering</span><span class="o">.</span><span class="n">labels_</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+</section>
+
+
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+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
+          (function () {
+            var url = 'https://www.intel.com/content/dam/www/global/wap/tms-loader.js'; // WAP file URL
+            var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = url;
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+      </script>
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+</head>
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+<body class="wy-body-for-nav"> 
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+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
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+<li><p><a class="reference external" href="https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae">From Hours to Minutes: 600x Faster SVM</a>,</p></li>
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+    <script>
+      current_version = "2025.1"
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+      Choose version  <!-- this text may get changed later by javascript -->
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+      role="listbox" aria-labelledby="">
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+  <section id="how-to-contribute">
+<h1>How to Contribute<a class="headerlink" href="#how-to-contribute" title="Link to this heading"></a></h1>
+<p>As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn.
+This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.</p>
+<section id="licensing">
+<h2>Licensing<a class="headerlink" href="#licensing" title="Link to this heading"></a></h2>
+<p>Intel(R) Extension for Scikit-learn uses the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE">Apache 2.0 License</a>. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.</p>
+</section>
+<section id="pull-requests">
+<h2>Pull Requests<a class="headerlink" href="#pull-requests" title="Link to this heading"></a></h2>
+<p>No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.</p>
+<p>Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">name</span> <span class="s2">&quot;Kate Developer&quot;</span>
+<span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">email</span> <span class="n">kate</span><span class="o">.</span><span class="n">developer</span><span class="nd">@company</span><span class="o">.</span><span class="n">com</span>
+</pre></div>
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+<section id="before-contributing-changes">
+<h3>Before Contributing Changes<a class="headerlink" href="#before-contributing-changes" title="Link to this heading"></a></h3>
+<p>Make sure you can build the product and run all the tests with your patch.
+For a larger feature, provide a relevant test.
+Document your code. Our project uses reStructuredText for documentation.
+For new file(s), specify the appropriate copyright year in the first line.
+Submit a pull request into the master branch.
+Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.</p>
+</section>
+</section>
+<section id="code-style">
+<h2>Code Style<a class="headerlink" href="#code-style" title="Link to this heading"></a></h2>
+<p>We use <a class="reference external" href="https://black.readthedocs.io/en/stable/">black</a> and <a class="reference external" href="https://pycqa.github.io/isort/">isort</a> formatters for Python* code.
+The line length is 90 characters; use default options otherwise. You can find the linter configuration in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml">.pyproject.toml</a>.</p>
+<p>A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.</p>
+<p>Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pre</span><span class="o">-</span><span class="n">commit</span>
+<span class="n">pre</span><span class="o">-</span><span class="n">commit</span> <span class="n">install</span>
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+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li class="breadcrumb-item active">Distributed Mode</li>
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+  <section id="distributed-mode">
+<span id="distributed"></span><h1>Distributed Mode<a class="headerlink" href="#distributed-mode" title="Link to this heading"></a></h1>
+<p>Intel® Extension for Scikit-learn* offers Single Program, Multiple Data (SPMD) supported interfaces for distributed computing.
+Several <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/oneapi-gpu.html#">GPU-supported algorithms</a>
+also provide distributed, multi-GPU computing capabilities via integration with <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code>. The prerequisites
+match those of GPU computing, along with an MPI backend of your choice (<a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html#gs.dcan6r">Intel MPI recommended</a>, available
+via <code class="docutils literal notranslate"><span class="pre">impi-devel</span></code> python package) and the <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code> python package. If using Intel® Extension for Scikit-learn*
+<a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md#build-from-sources">installed from sources</a>,
+ensure that the spmd_backend is built.</p>
+<p>Note that Intel® Extension for Scikit-learn* now supports GPU offloading to speed up MPI operations. This is supported automatically with
+some MPI backends, but in order to use GPU offloading with Intel MPI, set the following environment variable (providing
+data on device without this may lead to a runtime error):</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">I_MPI_OFFLOAD</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+<p>Estimators can be imported from the <code class="docutils literal notranslate"><span class="pre">sklearnex.spmd</span></code> module. Data should be distributed across multiple nodes as
+desired, and should be transfered to a dpctl or dpnp array before being passed to the estimator. View a full
+example of this process in the Intel® Extension for Scikit-learn* repository, where many examples of our SPMD-supported estimators are
+available: <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/examples/sklearnex/">https://github.com/intel/scikit-learn-intelex/blob/main/examples/sklearnex/</a>. To run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mpirun</span> <span class="o">-</span><span class="n">n</span> <span class="mi">4</span> <span class="n">python</span> <span class="n">linear_regression_spmd</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+<p>Note that additional mpirun arguments can be added as desired. SPMD-supported estimators are listed in the
+<a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html#spmd-support">algorithms support documentation</a>.</p>
+<p>Additionally, daal4py offers some distributed functionality, see
+<a class="reference external" href="https://intelpython.github.io/daal4py/scaling.html">documentation</a> for further details.</p>
+</section>
+
+
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
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+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
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+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
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+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ 
+</div>
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics">BasicStatistics (class in sklearnex.basic_statistics)</a>
+</li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_">batch_size_ (sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_">(sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_">(sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_">coef_ (sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_">covariance_ (sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit">fit() (sklearnex.basic_statistics.BasicStatistics method)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit">(sklearnex.basic_statistics.IncrementalBasicStatistics method)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit">(sklearnex.covariance.IncrementalEmpiricalCovariance method)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit">(sklearnex.linear_model.IncrementalLinearRegression method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics">IncrementalBasicStatistics (class in sklearnex.basic_statistics)</a>
+</li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance">IncrementalEmpiricalCovariance (class in sklearnex.covariance)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression">IncrementalLinearRegression (class in sklearnex.linear_model)</a>
+</li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_">intercept_ (sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_">location_ (sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_">max_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_">mean_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_">min_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_">n_features_in_ (sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_">(sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_">(sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_">n_samples_seen_ (sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_">(sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_">(sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit">partial_fit() (sklearnex.basic_statistics.IncrementalBasicStatistics method)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit">(sklearnex.covariance.IncrementalEmpiricalCovariance method)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit">(sklearnex.linear_model.IncrementalLinearRegression method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict">predict() (sklearnex.linear_model.IncrementalLinearRegression method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_">second_order_raw_moment_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_">standard_deviation_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_">sum_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_">sum_squares_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_">sum_squares_centered_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_">variance_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_">variation_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+
+
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+          </div>
+          <footer>
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+</ul>
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+
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+             
+  <section id="tuning-guide">
+<h1>Tuning Guide<a class="headerlink" href="#tuning-guide" title="Link to this heading"></a></h1>
+<p>The performance of some algorithms changes based on the parameters that are used.
+This section denotes the details of such cases.</p>
+<p>Refer to <a class="reference internal" href="../algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a> to see the full list of algorithms, parameters, and data formats supported in Intel® Extension for Scikit-learn*.</p>
+<section id="tsne">
+<span id="acceleration-tsne"></span><h2>TSNE<a class="headerlink" href="#tsne" title="Link to this heading"></a></h2>
+<p>TSNE algorithm consists of two components: KNN and Gradient Descent.
+The overall acceleration of TSNE depends on the acceleration of each of these algorithms.</p>
+<ul class="simple">
+<li><p>The KNN part of the algorithm supports all parameters except:</p>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</li>
+<li><p>The Gradient Descent part of the algorithm supports all parameters except:</p>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">n_components</span></code> = <cite>3</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">method</span></code> = <cite>‘exact’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">verbose</span></code> != <cite>0</cite></p></li>
+</ul>
+</li>
+</ul>
+<p>To get better performance, use parameters supported by both components.</p>
+</section>
+<section id="random-forest">
+<span id="acceleration-rf"></span><h2>Random Forest<a class="headerlink" href="#random-forest" title="Link to this heading"></a></h2>
+<p>Random Forest models accelerated with Intel® Extension for Scikit-learn* and using the <cite>hist</cite> splitting
+method discretize training data by creating a histogram with a configurable
+number of bins. The following keyword arguments can be used to influence the
+created histogram.</p>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 16.7%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Keyword argument</p></th>
+<th class="head"><p>Possible values</p></th>
+<th class="head"><p>Default value</p></th>
+<th class="head"><p>Description</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">maxBins</span></code></p></td>
+<td><p><cite>[0, inf)</cite></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">256</span></code></p></td>
+<td><p>Number of bins in the histogram with the discretized training data. The
+value <code class="docutils literal notranslate"><span class="pre">0</span></code> disables data discretization.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">minBinSize</span></code></p></td>
+<td><p><cite>[1, inf)</cite></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">5</span></code></p></td>
+<td><p>Minimum number of training data points in each bin after discretization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">binningStrategy</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">quantiles,</span> <span class="pre">averages</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">quantiles</span></code></p></td>
+<td><p>Selects the algorithm used to calculate bin edges. <code class="docutils literal notranslate"><span class="pre">quantiles</span></code>
+results in bins with a similar amount of training data points. <code class="docutils literal notranslate"><span class="pre">averages</span></code>
+divides the range of values observed in the training data set into
+equal-width bins of size <cite>(max - min) / maxBins</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+<p>Note that using discretized training data can greatly accelerate model training
+times, especially for larger data sets. However, due to the reduced fidelity of
+the data, the resulting model can present worse performance metrics compared to
+a model trained on the original data. In such cases, the number of bins can be
+increased with the <code class="docutils literal notranslate"><span class="pre">maxBins</span></code> parameter, or binning can be disabled entirely by
+setting <code class="docutils literal notranslate"><span class="pre">maxBins=0</span></code>.</p>
+</section>
+</section>
+
+
+           </div>
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+            Intel(R) Extension for Scikit-learn*
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
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+          <a href="#">Intel(R) Extension for Scikit-learn*</a>
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+            <a href="_sources/index.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="intelex">
+<span id="index"></span><h1>Intel® Extension for Scikit-learn*<a class="headerlink" href="#intelex" title="Link to this heading"></a></h1>
+<p>Intel(R) Extension for Scikit-learn is a <strong>free software AI accelerator</strong> designed to deliver up to <strong>100X</strong> faster performance for your existing scikit-learn code.
+The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.</p>
+<p class="rubric">Designed for Data Scientists and Framework Designers</p>
+<p>Use Intel(R) Extension for Scikit-learn, to:</p>
+<ul class="simple">
+<li><p>Speed up training and inference by up to 100x with the equivalent mathematical accuracy</p></li>
+<li><p>Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs</p></li>
+<li><p>Integrate the extension into your existing Scikit-learn applications without code modifications</p></li>
+<li><p>Enable and disable the extension with a couple of lines of code or at the command line</p></li>
+</ul>
+<p>Intel(R) Extension for Scikit-learn is also a part of <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html">Intel(R) AI Tools</a>.</p>
+<a class="reference internal image-reference" href="_images/scikit-learn-acceleration.PNG"><img alt="_images/scikit-learn-acceleration.PNG" src="_images/scikit-learn-acceleration.PNG" style="width: 800px;" />
+</a>
+<p>These performance charts use benchmarks that you can find in the <a class="reference external" href="https://github.com/IntelPython/scikit-learn_bench">scikit-learn bench repository</a>.</p>
+<section id="supported-algorithms">
+<h2>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Link to this heading"></a></h2>
+<p>See all of the <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a>.</p>
+</section>
+<section id="intel-r-optimizations">
+<h2>Intel(R) Optimizations<a class="headerlink" href="#intel-r-optimizations" title="Link to this heading"></a></h2>
+<section id="enable-intel-r-cpu-optimizations">
+<h3>Enable Intel(R) CPU Optimizations<a class="headerlink" href="#enable-intel-r-cpu-optimizations" title="Link to this heading"></a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="enable-intel-r-gpu-optimizations">
+<h3>Enable Intel(R) GPU optimizations<a class="headerlink" href="#enable-intel-r-gpu-optimizations" title="Link to this heading"></a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">dpctl</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span><span class="p">,</span> <span class="n">config_context</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">target_offload</span><span class="o">=</span><span class="s2">&quot;gpu:0&quot;</span><span class="p">):</span>
+    <span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
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+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Supported input types</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/input-types.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="supported-input-types">
+<span id="input-types"></span><h1>Supported input types<a class="headerlink" href="#supported-input-types" title="Link to this heading"></a></h1>
+<p>Just like Scikit-Learn estimators, estimators from Intel(R)’s Extension are able to accept
+and work with different classes of input data, including:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://numpy.org/">Numpy</a> arrays.</p></li>
+<li><p>Other array classes implementing the <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API</a> protocol
+(see <a class="reference internal" href="array_api.html#array-api"><span class="std std-ref">Array API support</span></a> for details).</p></li>
+<li><p><a class="reference external" href="https://docs.scipy.org/doc/scipy/tutorial/sparse.html">SciPy sparse arrays and sparse matrices</a> (depending on the estimator).</p></li>
+<li><p><a class="reference external" href="https://pandas.pydata.org">Pandas</a> <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and <code class="docutils literal notranslate"><span class="pre">Series</span></code> classes.</p></li>
+</ul>
+<p>In addition, Intel(R)’s Extension also supports:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp</a> arrays.</p></li>
+<li><p><a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> arrays.</p></li>
+</ul>
+<p>Stock Scikit-Learn estimators, depending on the version, might offer support for additional
+input types beyond this list, such as <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and <code class="docutils literal notranslate"><span class="pre">Series</span></code> classes from other libraries
+like <a class="reference external" href="https://pola.rs">Polars</a>.</p>
+<p>Intel(R)’s Extension currently does not offer accelerated routines for input types not listed
+here - when receiving an unsupported class, estimators will fall back to stock Scikit-Learn to
+handle it, so make sure to convert them to a supported type when using Intel(R)’s Extension.</p>
+</section>
+
+
+           </div>
+          </div>
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+  <section id="kaggle-kernels">
+<span id="kaggle"></span><h1>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Link to this heading"></a></h1>
+<p>See Kaggle notebooks created by Intel® Extension for Scikit-learn* developers.</p>
+<section id="acceleration">
+<h2>Acceleration<a class="headerlink" href="#acceleration" title="Link to this heading"></a></h2>
+<p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook">Introduction to scikit-learn-intelex</a>
+provides a summary of the speedup you can achieve with Intel® Extension for Scikit-learn*.</p>
+</section>
+<section id="machine-learning-workflows">
+<h2>Machine Learning Workflows<a class="headerlink" href="#machine-learning-workflows" title="Link to this heading"></a></h2>
+<p>Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.</p>
+<section id="kaggle-kernels-that-use-scikit-learn-and-intelex">
+<h3>Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-scikit-learn-and-intelex" title="Link to this heading"></a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="kaggle-kernels-that-use-automl-with-intelex">
+<h3>Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-automl-with-intelex" title="Link to this heading"></a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
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+          <li class="breadcrumb-item"><a href="../kaggle.html">Kaggle Kernels</a></li>
+      <li class="breadcrumb-item active">Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*</li>
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+            <a href="../_sources/kaggle/automl.rst.txt" rel="nofollow"> View page source</a>
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+  <section id="kaggle-kernels-that-use-automl-and-intelex">
+<h1>Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*<a class="headerlink" href="#kaggle-kernels-that-use-automl-and-intelex" title="Link to this heading"></a></h1>
+<p>The following Kaggle kernels show how to patch autoML frameworks with Intel® Extension for Scikit-learn*.</p>
+<p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 66.7%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alex97andreev/tps-jun-autogluon-with-sklearnex">AutoML MultiClass Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Jun 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/lordozvlad/titanic-automl-with-intel-extension-for-sklearn/notebook">AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> Titanic datset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/lordozvlad/fast-automl-with-intel-extension-for-scikit-learn/notebook">AutoML Binary Classification (Gradient Boosting, Random Forest) using AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Oct 2021] Synthetic molecular response data</p>
+</td>
+<td><p>Predict the biological response of molecules given various chemical properties</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/lordozvlad/tps-nov-automl-with-intel-extension">AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using EvalML and AutoGluon with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Nov 2021] Synthetic spam emails data</p>
+</td>
+<td><p>Identify spam emails via features extracted from the email</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex/notebook">AutoML Binary Classification (Random Forest, SVR, Blending) using PyCaret with Intel® Extension for Scikit-learn*</a></p>
+<p><strong>Data:</strong> [TPS Jan 2022] Fictional Sales data</p>
+</td>
+<td><p>Predict the corresponding item sales for each date-country-store-item combination</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+          <li class="breadcrumb-item"><a href="../kaggle.html">Kaggle Kernels</a></li>
+      <li class="breadcrumb-item active">Kaggle Kernels for Classification Tasks</li>
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+             
+  <section id="kaggle-kernels-for-classification-tasks">
+<h1>Kaggle Kernels for Classification Tasks<a class="headerlink" href="#kaggle-kernels-for-classification-tasks" title="Link to this heading"></a></h1>
+<p>The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various classification tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.</p>
+<p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
+<section id="binary-classification">
+<h2>Binary Classification<a class="headerlink" href="#binary-classification" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-nov-log-regression-with-sklearnex-17x-speedup">Logistic Regression for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Nov 2021] Synthetic spam emails data</p>
+</td>
+<td><p>Identify spam emails via features extracted from the email</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing (normalization)</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/fast-feature-importance-using-scikit-learn-intelex/notebook">Feature Importance in Random Forest for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Nov 2021] Synthetic spam emails data</p>
+</td>
+<td><p>Identify spam emails via features extracted from the email</p></td>
+<td><ul class="simple">
+<li><p>reducing DataFrame memory usage</p></li>
+<li><p>computing feature importance with ELI5 and the default scikit-learn permutation importance</p></li>
+<li><p>training using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/andreyrus/tps-apr-rf-with-intel-extension-for-scikit-learn">Random Forest for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Apr 2021] Synthetic data based on Titanic dataset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature construction</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/tps04-svm-with-intel-extension-for-scikit-learn">Support Vector Classification (SVC) for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Apr 2021] Synthetic data based on Titanic dataset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/tps04-svm-with-scikit-learn-intelex">Support Vector Classification (SVC) with Feature Preprocessing for Binary Classification</a></p>
+<p><strong>Data:</strong> [TPS Apr 2021] Synthetic data based on Titanic dataset</p>
+</td>
+<td><p>Predict whether a passenger survivies</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature engineering</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="multiclass-classification">
+<h2>MultiClass Classification<a class="headerlink" href="#multiclass-classification" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/tps-jun-fast-logreg-with-scikit-learn-intelex">Logistic Regression for MultiClass Classification with Quantile Transformer</a></p>
+<p><strong>Data:</strong> [TPS Jun 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing with Quantile Transformer</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex">Support Vector Classification (SVC) for MultiClass Classification (example 1)</a></p>
+<p><strong>Data:</strong> [TPS May 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/owerbat/tps-jun-fast-stacking-with-scikit-learn-intelex">Stacking Classifer with Logistic Regression, kNN, Random Forest, and Quantile Transformer</a></p>
+<p><strong>Data:</strong> [TPS Jun 2021] Synthetic eCommerce data</p>
+</td>
+<td><p>Predict the category of an eCommerce product</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing: one-hot encoding, dimensionality reduction with PCA, normalization</p></li>
+<li><p>creating a stacking classifier with logistic regression, kNN, and random forest,
+and a pipeline of Quantile Transformer and another logistic regression as a final estimator</p></li>
+<li><p>searching for optimal parameters for the stacking classifier</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup">Support Vector Classification (SVC) for MultiClass Classification (example 2)</a></p>
+<p><strong>Data:</strong> [TPS Dec 2021] Synthetic Forest Cover Type data</p>
+</td>
+<td><p>Predict the forest cover type</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/tps-dec-fast-feature-importance-with-sklearnex">Feature Importance in Random Forest for MultiClass Classification</a></p>
+<p><strong>Data:</strong> [TPS Dec 2021] Synthetic Forest Cover Type data</p>
+</td>
+<td><p>Predict the forest cover type</p></td>
+<td><ul class="simple">
+<li><p>reducing DataFrame memory usage</p></li>
+<li><p>computing feature importance with ELI5</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/alexeykolobyanin/tps-feb-knn-with-sklearnex-13x-speedup">k-Nearest Neighbors (kNN) for MultiClass Classification</a></p>
+<p><strong>Data:</strong> [TPS Feb 2022] Bacteria DNA</p>
+</td>
+<td><p>Predict bacteria species based on repeated lossy measurements of DNA snippets</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="classification-tasks-in-computer-vision">
+<h2>Classification Tasks in Computer Vision<a class="headerlink" href="#classification-tasks-in-computer-vision" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-mnist?scriptVersionId=58739300">Support Vector Classification (SVC) for MultiClass Classification (CV task)</a></p>
+<p><strong>Data:</strong> Digit Recognizer (MNIST)</p>
+</td>
+<td><p>Recognize hand-written digits</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/fast-knn-using-scikit-learn-intelex-for-mnist?scriptVersionId=58738635">k-Nearest Neighbors (kNN) for MultiClass Classification (CV task)</a></p>
+<p><strong>Data:</strong> Digit Recognizer (MNIST)</p>
+</td>
+<td><p>Recognize hand-written digits</p></td>
+<td><ul class="simple">
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="classification-tasks-in-natural-language-processing">
+<h2>Classification Tasks in Natural Language Processing<a class="headerlink" href="#classification-tasks-in-natural-language-processing" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-nlp?scriptVersionId=58739339">Support Vector Classification (SVC) for a Binary Classification (NLP task)</a></p>
+<p><strong>Data:</strong> Natural Language Processing with Disaster Tweets</p>
+</td>
+<td><p>Predict which tweets are about real disasters and which ones are not</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>TF-IDF calculation</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/using-scikit-learn-intelex-for-what-s-cooking?scriptVersionId=58739642">One-vs-Rest Support Vector Machine (SVM) with Text Data for MultiClass Classification</a></p>
+<p><strong>Data:</strong> What’s Cooking</p>
+</td>
+<td><p>Use recipe ingredients to predict the cuisine</p></td>
+<td><ul class="simple">
+<li><p>feature extraction using TfidfVectorizer</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/code/alex97andreev/fast-svm-for-sparse-data-from-nlp-problem">Support Vector Classification (SVC) for Binary Classification with Sparse Data (NLP task)</a></p>
+<p><strong>Data:</strong> Stack Overflow questions</p>
+</td>
+<td><p>Predict the binary quality rating for Stack Overflow questions</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>TF-IDF calculation</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
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+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
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+  <section id="kaggle-kernels-for-regression-tasks">
+<h1>Kaggle Kernels for Regression Tasks<a class="headerlink" href="#kaggle-kernels-for-regression-tasks" title="Link to this heading"></a></h1>
+<p>The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various regression tasks.
+These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.</p>
+<p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
+<section id="using-a-single-regressor">
+<h2>Using a Single Regressor<a class="headerlink" href="#using-a-single-regressor" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/kppetrov/tps-jul-2021-baseline-with-nusvr-model/notebook">Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>search for optimal paramters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-aug-nusvr-with-intel-extension-for-sklearn">Nu Support Vector Regression (nuSVR)</a></p>
+<p><strong>Data:</strong> [TPS Aug 2021] Synthetic loan data</p>
+</td>
+<td><p>Calculate loss associated with a loan defaults</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature engineering</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/house-prices-nusvr-sklearn-intelex-4x-speedup">Nu Support Vector Regression (nuSVR)</a></p>
+<p><strong>Data:</strong> House Prices dataset</p>
+</td>
+<td><p>Predict sale prices for a property based on its characteristics</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>exploring outliers</p></li>
+<li><p>feature engineering</p></li>
+<li><p>filling missing values</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/pahandrovich/tps-jul-2021-fast-randomforest-with-sklearnex">Random Forest Regression</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>checking correlation between features</p></li>
+<li><p>search for best paramters using GridSearchCV</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-jul-rf-with-intel-extension-for-scikit-learn">Random Forest Regression with Feature Engineering</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>feature engineering</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/tps-mar-fast-workflow-using-scikit-learn-intelex">Random Forest Regression with Feature Importance Computation</a></p>
+<p><strong>Data:</strong> [TPS Mar 2022] Spatio-temporal traffic data</p>
+</td>
+<td><p>Forecast twelve-hours of traffic flow in a major U.S. metropolitan area</p></td>
+<td><ul class="simple">
+<li><p>feature engineering</p></li>
+<li><p>computing feature importance with ELI5</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-sep-ridge-with-sklearn-intelex-2x-speedup">Ridge Regression</a></p>
+<p><strong>Data:</strong> [TPS Sep 2021] Synthetic insurance data</p>
+</td>
+<td><p>Predict the probability of a customer making a claim upon an insurance policy</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>filling missing values</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="stacking-regressors">
+<h2>Stacking Regressors<a class="headerlink" href="#stacking-regressors" title="Link to this heading"></a></h2>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Kernel</p></th>
+<th class="head"><p>Goal</p></th>
+<th class="head"><p>Content</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/tps-jul-stacking-with-scikit-learn-intelex">Stacking Regressor with Random Fores, SVR, and LASSO</a></p>
+<p><strong>Data:</strong> [TPS Jul 2021] Synthetic pollution data</p>
+</td>
+<td><p>Predict air pollution measurements over time based on weather and input values from multiple sensors</p></td>
+<td><ul class="simple">
+<li><p>feature engineering</p></li>
+<li><p>creating a stacking regressor</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/alexeykolobyanin/predict-sales-stacking-with-scikit-learn-intelex">Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data</a></p>
+<p><strong>Data:</strong> Predict Future Sales dataset</p>
+</td>
+<td><p>Predict total sales for every product and store in the next month based on daily sales data</p></td>
+<td><ul class="simple">
+<li><p>data preprocessing</p></li>
+<li><p>creating a stacking regressor</p></li>
+<li><p>search for optimal parameters using Optuna</p></li>
+<li><p>training and prediction using scikit-learn-intelex</p></li>
+<li><p>performance comparison to scikit-learn</p></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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diff --git a/2025.1/non-scikit-algorithms.html b/2025.1/non-scikit-algorithms.html
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+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
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+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
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+      <li class="breadcrumb-item active">Non-Scikit-Learn Algorithms</li>
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+            <a href="_sources/non-scikit-algorithms.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="non-scikit-learn-algorithms">
+<h1>Non-Scikit-Learn Algorithms<a class="headerlink" href="#non-scikit-learn-algorithms" title="Link to this heading"></a></h1>
+<p>Algorithms not presented in the original scikit-learn are described here. All algorithms are
+available for both CPU and GPU (including distributed mode)</p>
+<section id="basicstatistics">
+<h2>BasicStatistics<a class="headerlink" href="#basicstatistics" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.basic_statistics.</span></span><span class="sig-name descname"><span class="pre">BasicStatistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/basic_statistics.html#BasicStatistics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics" title="Link to this definition"></a></dt>
+<dd><p>Estimator for basic statistics.
+Allows to compute basic statistics for provided data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>result_options</strong> (<em>string</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>, </em><em>default='all'</em>) – Used to set statistics to calculate. Possible values are <code class="docutils literal notranslate"><span class="pre">'min'</span></code>, <code class="docutils literal notranslate"><span class="pre">'max'</span></code>, <code class="docutils literal notranslate"><span class="pre">'sum'</span></code>, <code class="docutils literal notranslate"><span class="pre">'mean'</span></code>, <code class="docutils literal notranslate"><span class="pre">'variance'</span></code>,
+<code class="docutils literal notranslate"><span class="pre">'variation'</span></code>, <code class="docutils literal notranslate"><span class="pre">sum_squares'</span></code>, <code class="docutils literal notranslate"><span class="pre">sum_squares_centered'</span></code>, <code class="docutils literal notranslate"><span class="pre">'standard_deviation'</span></code>, <code class="docutils literal notranslate"><span class="pre">'second_order_raw_moment'</span></code>
+or a list containing any of these values. If set to <code class="docutils literal notranslate"><span class="pre">'all'</span></code> then all possible statistics will be
+calculated.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use in parallel for the computation.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means using all physical cores
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all logical cores.
+See <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-n_jobs" title="(in scikit-learn v1.6)"><span class="xref std std-term">Glossary</span></a> for more details.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.min_">
+<span class="sig-name descname"><span class="pre">min_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.min_" title="Link to this definition"></a></dt>
+<dd><p>Minimum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.max_">
+<span class="sig-name descname"><span class="pre">max_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.max_" title="Link to this definition"></a></dt>
+<dd><p>Maximum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.sum_">
+<span class="sig-name descname"><span class="pre">sum_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.sum_" title="Link to this definition"></a></dt>
+<dd><p>Sum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.mean_">
+<span class="sig-name descname"><span class="pre">mean_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.mean_" title="Link to this definition"></a></dt>
+<dd><p>Mean of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.variance_">
+<span class="sig-name descname"><span class="pre">variance_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.variance_" title="Link to this definition"></a></dt>
+<dd><p>Variance of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.variation_">
+<span class="sig-name descname"><span class="pre">variation_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.variation_" title="Link to this definition"></a></dt>
+<dd><p>Variation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.sum_squares_">
+<span class="sig-name descname"><span class="pre">sum_squares_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_" title="Link to this definition"></a></dt>
+<dd><p>Sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.standard_deviation_">
+<span class="sig-name descname"><span class="pre">standard_deviation_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.standard_deviation_" title="Link to this definition"></a></dt>
+<dd><p>Standard deviation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_">
+<span class="sig-name descname"><span class="pre">sum_squares_centered_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_" title="Link to this definition"></a></dt>
+<dd><p>Centered sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_">
+<span class="sig-name descname"><span class="pre">second_order_raw_moment_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_" title="Link to this definition"></a></dt>
+<dd><p>Second order moment of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Attribute exists only if corresponding result option has been provided.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Names of attributes without the trailing underscore are
+supported currently but deprecated in 2025.1 and will be removed in 2026.0</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Some results can exhibit small variations due to
+floating point error accumulation and multithreading.</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.basic_statistics</span> <span class="kn">import</span> <span class="n">BasicStatistics</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span> <span class="o">=</span> <span class="n">BasicStatistics</span><span class="p">(</span><span class="n">result_options</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sum&#39;</span><span class="p">,</span> <span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span><span class="o">.</span><span class="n">sum_</span>
+<span class="go">np.array([4., 6.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span><span class="o">.</span><span class="n">min_</span>
+<span class="go">np.array([1., 2.])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.fit">
+<span class="sig-prename descclassname"><span class="pre">BasicStatistics.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/basic_statistics.html#BasicStatistics.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.fit" title="Link to this definition"></a></dt>
+<dd><p>Calculate statistics of X.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Data for compute, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>sample_weight</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Weights for compute weighted statistics, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="incrementalbasicstatistics">
+<h2>IncrementalBasicStatistics<a class="headerlink" href="#incrementalbasicstatistics" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.basic_statistics.</span></span><span class="sig-name descname"><span class="pre">IncrementalBasicStatistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/incremental_basic_statistics.html#IncrementalBasicStatistics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics" title="Link to this definition"></a></dt>
+<dd><p>Calculates basic statistics on the given data, allows for computation when the data are split into
+batches. The user can use <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method to provide a single batch of data or use the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method to provide
+the entire dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>result_options</strong> (<em>string</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>, </em><em>default='all'</em>) – List of statistics to compute</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of samples to use for each batch. Only used when calling
+<code class="docutils literal notranslate"><span class="pre">fit</span></code>. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>
+is inferred from the data and set to <code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">*</span> <span class="pre">n_features</span></code>.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use in parallel for the computation.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means using all physical cores
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all logical cores.
+See <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-n_jobs" title="(in scikit-learn v1.6)"><span class="xref std std-term">Glossary</span></a> for more details.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.min_">
+<span class="sig-name descname"><span class="pre">min_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.min_" title="Link to this definition"></a></dt>
+<dd><p>Minimum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.max_">
+<span class="sig-name descname"><span class="pre">max_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.max_" title="Link to this definition"></a></dt>
+<dd><p>Maximum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.sum_">
+<span class="sig-name descname"><span class="pre">sum_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_" title="Link to this definition"></a></dt>
+<dd><p>Sum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.mean_">
+<span class="sig-name descname"><span class="pre">mean_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_" title="Link to this definition"></a></dt>
+<dd><p>Mean of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.variance_">
+<span class="sig-name descname"><span class="pre">variance_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_" title="Link to this definition"></a></dt>
+<dd><p>Variance of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.variation_">
+<span class="sig-name descname"><span class="pre">variation_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_" title="Link to this definition"></a></dt>
+<dd><p>Variation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_">
+<span class="sig-name descname"><span class="pre">sum_squares_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_" title="Link to this definition"></a></dt>
+<dd><p>Sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_">
+<span class="sig-name descname"><span class="pre">standard_deviation_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_" title="Link to this definition"></a></dt>
+<dd><p>Standard deviation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_">
+<span class="sig-name descname"><span class="pre">sum_squares_centered_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_" title="Link to this definition"></a></dt>
+<dd><p>Centered sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_">
+<span class="sig-name descname"><span class="pre">second_order_raw_moment_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_" title="Link to this definition"></a></dt>
+<dd><p>Second order moment of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_">
+<span class="sig-name descname"><span class="pre">n_samples_seen_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_" title="Link to this definition"></a></dt>
+<dd><p>The number of samples processed by the estimator. Will be reset on
+new calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>, but increments across <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> calls.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_">
+<span class="sig-name descname"><span class="pre">batch_size_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_" title="Link to this definition"></a></dt>
+<dd><p>Inferred batch size from <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_">
+<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_" title="Link to this definition"></a></dt>
+<dd><p>Number of features seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> or  <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Attribute exists only if corresponding result option has been provided.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Names of attributes without the trailing underscore are
+supported currently but deprecated in 2025.1 and will be removed in 2026.0</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.basic_statistics</span> <span class="kn">import</span> <span class="n">IncrementalBasicStatistics</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span> <span class="o">=</span> <span class="n">IncrementalBasicStatistics</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">sum_</span>
+<span class="go">np.array([4., 6.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">min_</span>
+<span class="go">np.array([1., 2.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">sum_</span>
+<span class="go">np.array([4., 6.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">max_</span>
+<span class="go">np.array([3., 4.])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalBasicStatistics.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/incremental_basic_statistics.html#IncrementalBasicStatistics.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.fit" title="Link to this definition"></a></dt>
+<dd><p>Calculate statistics of X using minibatches of size batch_size.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Data for compute, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>sample_weight</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Weights for compute weighted statistics, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalBasicStatistics.</span></span><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/incremental_basic_statistics.html#IncrementalBasicStatistics.partial_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit" title="Link to this definition"></a></dt>
+<dd><p>Incremental fit with X. All of X is processed as a single batch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Data for compute, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>sample_weight</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Weights for compute weighted statistics, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples.</p></li>
+<li><p><strong>check_input</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – Run <code class="docutils literal notranslate"><span class="pre">check_array</span></code> on X.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="incrementalempiricalcovariance">
+<h2>IncrementalEmpiricalCovariance<a class="headerlink" href="#incrementalempiricalcovariance" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.covariance.</span></span><span class="sig-name descname"><span class="pre">IncrementalEmpiricalCovariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/covariance/incremental_covariance.html#IncrementalEmpiricalCovariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance" title="Link to this definition"></a></dt>
+<dd><p>Maximum likelihood covariance estimator that allows for the estimation when the data are split into
+batches. The user can use the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method to provide a single batch of data or use the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method to provide
+the entire dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>store_precision</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=False</em>) – Specifies if the estimated precision is stored.</p></li>
+<li><p><strong>assume_centered</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=False</em>) – If True, data are not centered before computation.
+Useful when working with data whose mean is almost, but not exactly
+zero.
+If False (default), data are centered before computation.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of samples to use for each batch. Only used when calling
+<code class="docutils literal notranslate"><span class="pre">fit</span></code>. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>
+is inferred from the data and set to <code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">*</span> <span class="pre">n_features</span></code>, to provide a
+balance between approximation accuracy and memory consumption.</p></li>
+<li><p><strong>copy</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – If False, X will be overwritten. <code class="docutils literal notranslate"><span class="pre">copy=False</span></code> can be used to
+save memory but is unsafe for general use.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use in parallel for the computation.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means using all physical cores
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all logical cores.
+See <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-n_jobs" title="(in scikit-learn v1.6)"><span class="xref std std-term">Glossary</span></a> for more details.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.location_">
+<span class="sig-name descname"><span class="pre">location_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.location_" title="Link to this definition"></a></dt>
+<dd><p>Estimated location, i.e. the estimated mean.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_">
+<span class="sig-name descname"><span class="pre">covariance_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_" title="Link to this definition"></a></dt>
+<dd><p>Estimated covariance matrix</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features, n_features)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_">
+<span class="sig-name descname"><span class="pre">n_samples_seen_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_" title="Link to this definition"></a></dt>
+<dd><p>The number of samples processed by the estimator. Will be reset on
+new calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>, but increments across <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> calls.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_">
+<span class="sig-name descname"><span class="pre">batch_size_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_" title="Link to this definition"></a></dt>
+<dd><p>Inferred batch size from <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_">
+<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_" title="Link to this definition"></a></dt>
+<dd><p>Number of features seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.covariance</span> <span class="kn">import</span> <span class="n">IncrementalEmpiricalCovariance</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span> <span class="o">=</span> <span class="n">IncrementalEmpiricalCovariance</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">covariance_</span>
+<span class="go">np.array([[1., 1.],[1., 1.]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">location_</span>
+<span class="go">np.array([2., 3.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">covariance_</span>
+<span class="go">np.array([[1., 1.],[1., 1.]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">location_</span>
+<span class="go">np.array([2., 3.])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalEmpiricalCovariance.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/covariance/incremental_covariance.html#IncrementalEmpiricalCovariance.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.fit" title="Link to this definition"></a></dt>
+<dd><p>Fit the model with X, using minibatches of size batch_size.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <cite>n_samples</cite> is the number of samples and
+<cite>n_features</cite> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalEmpiricalCovariance.</span></span><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/covariance/incremental_covariance.html#IncrementalEmpiricalCovariance.partial_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit" title="Link to this definition"></a></dt>
+<dd><p>Incremental fit with X. All of X is processed as a single batch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <cite>n_samples</cite> is the number of samples and
+<cite>n_features</cite> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>check_input</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – Run check_array on X.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="incrementallinearregression">
+<h2>IncrementalLinearRegression<a class="headerlink" href="#incrementallinearregression" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.linear_model.</span></span><span class="sig-name descname"><span class="pre">IncrementalLinearRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression" title="Link to this definition"></a></dt>
+<dd><p>Trains a linear regression model, allows for computation if the data are split into
+batches. The user can use the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method to provide a single batch of data or use the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method to provide
+the entire dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fit_intercept</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>)</p></li>
+<li><p><strong>set</strong> (<em>Whether to calculate the intercept for this model. If</em>)</p></li>
+<li><p><strong>False</strong> (<em>to</em>)</p></li>
+<li><p><strong>calculations</strong> (<em>no intercept will be used in</em>)</p></li>
+<li><p><strong>centered</strong><strong>)</strong><strong>.</strong> (<em>(</em><em>i.e. data is expected to be</em>)</p></li>
+<li><p><strong>copy_X</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – If True, X will be copied; else, it may be overwritten.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use for the computation.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of samples to use for each batch. Only used when calling
+<code class="docutils literal notranslate"><span class="pre">fit</span></code>. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>
+is inferred from the data and set to <code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">*</span> <span class="pre">n_features</span></code>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.coef_">
+<span class="sig-name descname"><span class="pre">coef_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.coef_" title="Link to this definition"></a></dt>
+<dd><p>Estimated coefficients for the linear regression problem.
+If multiple targets are passed during the fit (y 2D), this
+is a 2D array of shape (n_targets, n_features), while if only
+one target is passed, this is a 1D array of length n_features.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array of shape (n_features, ) or (n_targets, n_features)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.intercept_">
+<span class="sig-name descname"><span class="pre">intercept_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.intercept_" title="Link to this definition"></a></dt>
+<dd><p>Independent term in the linear model. Set to 0.0 if
+<cite>fit_intercept = False</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a> or array of shape (n_targets,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_">
+<span class="sig-name descname"><span class="pre">n_samples_seen_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_" title="Link to this definition"></a></dt>
+<dd><p>The number of samples processed by the estimator. Will be reset on
+new calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>, but increments across <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> calls.
+It should be not less than <cite>n_features_in_</cite> if <cite>fit_intercept</cite>
+is False and not less than <cite>n_features_in_</cite> + 1 if <cite>fit_intercept</cite>
+is True to obtain regression coefficients.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.batch_size_">
+<span class="sig-name descname"><span class="pre">batch_size_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.batch_size_" title="Link to this definition"></a></dt>
+<dd><p>Inferred batch size from <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.n_features_in_">
+<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_" title="Link to this definition"></a></dt>
+<dd><p>Number of features seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.linear_model</span> <span class="kn">import</span> <span class="n">IncrementalLinearRegression</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span> <span class="o">=</span> <span class="n">IncrementalLinearRegression</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">10</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">,</span> <span class="mf">8.5</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span> <span class="n">y</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span> <span class="n">y</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">coef_</span>
+<span class="go">np.array([0.5., 0.5.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">intercept_</span>
+<span class="go">np.array(0.)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">coef_</span>
+<span class="go">np.array([0.5., 0.5.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">intercept_</span>
+<span class="go">np.array(0.)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalLinearRegression.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.fit" title="Link to this definition"></a></dt>
+<dd><p>Fit the model with X and y, using minibatches of size <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features. It is necessary for
+<code class="docutils literal notranslate"><span class="pre">n_samples</span></code> to be not less than <code class="docutils literal notranslate"><span class="pre">n_features</span></code> if <code class="docutils literal notranslate"><span class="pre">fit_intercept</span></code>
+is False and not less than <code class="docutils literal notranslate"><span class="pre">n_features</span> <span class="pre">+</span> <span class="pre">1</span></code> if <code class="docutils literal notranslate"><span class="pre">fit_intercept</span></code>
+is True</p></li>
+<li><p><strong>y</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>) or </em><em>(</em><em>n_samples</em><em>, </em><em>n_targets</em><em>)</em>) – Target values, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_targets</span></code> is the number of targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.partial_fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalLinearRegression.</span></span><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression.partial_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.partial_fit" title="Link to this definition"></a></dt>
+<dd><p>Incremental fit linear model with X and y. All of X and y is
+processed as a single batch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<cite>n_features</cite> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>) or </em><em>(</em><em>n_samples</em><em>, </em><em>n_targets</em><em>)</em>) – Target values, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_targets</span></code> is the number of targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.predict">
+<span class="sig-prename descclassname"><span class="pre">IncrementalLinearRegression.</span></span><span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.predict" title="Link to this definition"></a></dt>
+<dd><p>Predict using the linear model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> or </em><em>sparse matrix</em><em>, </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Samples.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>C</strong> – Returns predicted values.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array, shape (n_samples, n_targets)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
+
+
+           </div>
+          </div>
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diff --git a/2025.1/objects.inv b/2025.1/objects.inv
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@@ -0,0 +1,581 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <title>oneAPI and GPU support in Intel® Extension for Scikit-learn* &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
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+    <link rel="prev" title="Supported Algorithms" href="algorithms.html" />
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+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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+      </script>
+
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+  <div class="version-switcher__container dropdown">
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+      current_version = "2025.1"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
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+             
+  <section id="oneapi-and-gpu-support-in-intelex">
+<span id="oneapi-gpu"></span><h1>oneAPI and GPU support in Intel® Extension for Scikit-learn*<a class="headerlink" href="#oneapi-and-gpu-support-in-intelex" title="Link to this heading"></a></h1>
+<p>Intel® Extension for Scikit-learn* supports oneAPI concepts, which
+means that algorithms can be executed on different devices: CPUs and GPUs.
+This is done via integration with
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> package that
+implements core oneAPI concepts like queues and devices.</p>
+<section id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading"></a></h2>
+<p>For execution on GPU, DPC++ compiler runtime and driver are required. Refer to <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html">DPC++ system
+requirements</a> for details.</p>
+<p>DPC++ compiler runtime can be installed either from PyPI or Anaconda:</p>
+<ul>
+<li><p>Install from PyPI:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">dpcpp</span><span class="o">-</span><span class="n">cpp</span><span class="o">-</span><span class="n">rt</span>
+</pre></div>
+</div>
+</li>
+<li><p>Install using Conda via the Intel repository:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">dpcpp_cpp_rt</span> <span class="o">-</span><span class="n">c</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">software</span><span class="o">.</span><span class="n">repos</span><span class="o">.</span><span class="n">intel</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">python</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span>
+</pre></div>
+</div>
+</li>
+</ul>
+</section>
+<section id="device-offloading">
+<h2>Device offloading<a class="headerlink" href="#device-offloading" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn* offers two options for running an algorithm on a
+specific device with the help of dpctl:</p>
+<ul>
+<li><p>Pass input data as <a class="reference external" href="https://intelpython.github.io/dpctl/latest/docfiles/dpctl/usm_ndarray.html#dpctl.tensor.usm_ndarray">dpctl.tensor.usm_ndarray</a> to the algorithm.</p>
+<p>The computation will run on the device where the input data is
+located, and the result will be returned as <code class="code docutils literal notranslate"><span class="pre">usm_ndarray</span></code> to the same
+device.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>All the input data for an algorithm must reside on the same device.</p>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The <code class="code docutils literal notranslate"><span class="pre">usm_ndarray</span></code> can only be consumed by the base methods
+like <code class="code docutils literal notranslate"><span class="pre">fit</span></code>, <code class="code docutils literal notranslate"><span class="pre">predict</span></code>, and <code class="code docutils literal notranslate"><span class="pre">transform</span></code>.
+Note that only the algorithms in Intel® Extension for Scikit-learn* support
+<code class="code docutils literal notranslate"><span class="pre">usm_ndarray</span></code>. The algorithms from the stock version of scikit-learn
+do not support this feature.</p>
+</div>
+</li>
+<li><p>Use global configurations of Intel® Extension for Scikit-learn**:</p>
+<ol class="arabic simple">
+<li><p>The <code class="code docutils literal notranslate"><span class="pre">target_offload</span></code> option can be used to set the device primarily
+used to perform computations. Accepted data types are <code class="code docutils literal notranslate"><span class="pre">str</span></code> and
+<code class="code docutils literal notranslate"><span class="pre">dpctl.SyclQueue</span></code>. If you pass a string to <code class="code docutils literal notranslate"><span class="pre">target_offload</span></code>,
+it should either be <code class="docutils literal notranslate"><span class="pre">&quot;auto&quot;</span></code>, which means that the execution
+context is deduced from the location of input data, or a string
+with SYCL* filter selector. The default value is <code class="docutils literal notranslate"><span class="pre">&quot;auto&quot;</span></code>.</p></li>
+<li><p>The <code class="code docutils literal notranslate"><span class="pre">allow_fallback_to_host</span></code> option
+is a Boolean flag. If set to <code class="code docutils literal notranslate"><span class="pre">True</span></code>, the computation is allowed
+to fallback to the host device when a particular estimator does not support
+the selected device. The default value is <code class="code docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+</ol>
+</li>
+</ul>
+<p>These options can be set using <code class="code docutils literal notranslate"><span class="pre">sklearnex.set_config()</span></code> function or
+<code class="code docutils literal notranslate"><span class="pre">sklearnex.config_context</span></code>. To obtain the current values of these options,
+call <code class="code docutils literal notranslate"><span class="pre">sklearnex.get_config()</span></code>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Functions <code class="code docutils literal notranslate"><span class="pre">set_config</span></code>, <code class="code docutils literal notranslate"><span class="pre">get_config</span></code> and <code class="code docutils literal notranslate"><span class="pre">config_context</span></code>
+are always patched after the <code class="code docutils literal notranslate"><span class="pre">sklearnex.patch_sklearn()</span></code> call.</p>
+</div>
+<p class="rubric">Compatibility considerations</p>
+<p>For compatibility reasons, algorithms in Intel® Extension for Scikit-learn* may be offloaded to the device using
+<code class="code docutils literal notranslate"><span class="pre">daal4py.oneapi.sycl_context</span></code>. However, it is recommended to use one of the options
+described above for device offloading instead of using <code class="code docutils literal notranslate"><span class="pre">sycl_context</span></code>.</p>
+</section>
+<section id="example">
+<h2>Example<a class="headerlink" href="#example" title="Link to this heading"></a></h2>
+<p>An example on how to patch your code with Intel CPU/GPU optimizations:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span><span class="p">,</span> <span class="n">config_context</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">target_offload</span><span class="o">=</span><span class="s2">&quot;gpu:0&quot;</span><span class="p">):</span>
+   <span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Current offloading behavior restricts fitting and inference of any models to be
+in the same context or absence of context. For example, a model trained in the GPU context with
+target_offload=”gpu:0” throws an error if the inference is made outside the same GPU context.</p>
+</div>
+</section>
+</section>
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="preview-functionality">
+<span id="preview"></span><h1>Preview Functionality<a class="headerlink" href="#preview-functionality" title="Link to this heading"></a></h1>
+<p>Some of implemented functionality in Intel® Extension for Scikit-learn* doesn’t meet one or few of next requirements
+for being enabled by default for all users:</p>
+<ul class="simple">
+<li><p>The functionality API is not stable and can be changed in future</p></li>
+<li><p>The functionality doesn’t have full compatibility with stock Scikit-learn</p></li>
+<li><p>The functionality misses performance targets compared to stock Scikit-learn or previously available version of functionality</p></li>
+<li><p>The functionality is not fully tested</p></li>
+</ul>
+<p>This type of functionality is available under <strong>preview mode</strong> of Intel® Extension for Scikit-learn* and located in
+the corresponding module (<cite>sklearnex.preview</cite>).</p>
+<p>Preview functionality <em>may</em> or <em>may not</em> participate in patching of Scikit-learn.
+For example, a preview estimator may be a replacement for a stock one or a completely new one.</p>
+<p>To enable preview functionality, you need to set the <cite>SKLEARNEX_PREVIEW</cite> environment variable
+to non-empty value before patching of Scikit-learn.
+For example, you can set the environment variable in the following way:</p>
+<ul>
+<li><p>On Linux* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">SKLEARNEX_PREVIEW</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+</li>
+<li><p>On Windows* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span> <span class="n">SKLEARNEX_PREVIEW</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+</li>
+</ul>
+<p>Then, you can import Scikit-learn estimator patched with a preview one from <cite>sklearnex.preview</cite> module:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">IncrementalPCA</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">IncrementalPCA</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span>
+<span class="c1"># output:</span>
+<span class="c1"># sklearnex.preview.decomposition.incremental_pca</span>
+</pre></div>
+</div>
+<p>Current list of preview estimators:</p>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+<col style="width: 16.7%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Estimator name</p></th>
+<th class="head"><p>Module</p></th>
+<th class="head"><p>Is patching supported</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>EmpiricalCovariance</p></td>
+<td><p>sklearnex.preview.covariance</p></td>
+<td><p>Yes</p></td>
+</tr>
+<tr class="row-odd"><td><p>IncrementalPCA</p></td>
+<td><p>sklearnex.preview.decomposition</p></td>
+<td><p>Yes</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Quick Start</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/quick-start.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="quick-start">
+<h1>Quick Start<a class="headerlink" href="#quick-start" title="Link to this heading"></a></h1>
+<p>Get ready to elevate your scikit-learn code with Intel® Extension for Scikit-learn* and experience the benefits of accelerated performance in just a few simple steps.</p>
+<section id="compatibility-with-scikit-learn">
+<h2>Compatibility with Scikit-learn*<a class="headerlink" href="#compatibility-with-scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.</p>
+</section>
+<section id="integrate-intelex">
+<h2>Integrate Intel® Extension for Scikit-learn*<a class="headerlink" href="#integrate-intelex" title="Link to this heading"></a></h2>
+<section id="patching">
+<h3>Patching<a class="headerlink" href="#patching" title="Link to this heading"></a></h3>
+<p>Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension.
+This action is called <code class="docutils literal notranslate"><span class="pre">patching</span></code>. This is not a permanent change so you can always undo the patching if necessary.</p>
+<p>To patch Intel® Extension for Scikit-learn, use one of these methods:</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Method</p></th>
+<th class="head"><p>Action</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Use a flag in the command line</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span> <span class="n">my_application</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+</td>
+</tr>
+<tr class="row-odd"><td><p>Modify your script</p></td>
+<td><p>Add the following lines:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</td>
+</tr>
+<tr class="row-even"><td><p>Import an estimator from the <code class="docutils literal notranslate"><span class="pre">sklearnex</span></code> module</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
+</pre></div>
+</div>
+</td>
+</tr>
+</tbody>
+</table>
+<p>These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.</p>
+<p><strong>Example</strong></p>
+<p>This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.</p>
+<div class="literal-block-wrapper docutils container" id="id2">
+<div class="code-block-caption"><span class="caption-text">Example: Drop-In Patching</span><a class="headerlink" href="#id2" title="Link to this code"></a></div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>  <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+  <span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+  <span class="n">patch_sklearn</span><span class="p">()</span>
+
+  <span class="c1"># You need to re-import scikit-learn algorithms after the patch</span>
+  <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+  <span class="c1"># The use of the original Scikit-learn is not changed</span>
+  <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">0</span><span class="p">],</span>
+              <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
+  <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+  <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;kmeans.labels_ = </span><span class="si">{</span><span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="global-patching">
+<h3>Global Patching<a class="headerlink" href="#global-patching" title="Link to this heading"></a></h3>
+<p>You can also use global patching to patch all your scikit-learn applications without any additional actions.</p>
+<p>Before you begin, make sure that you have read and write permissions for Scikit-learn files.</p>
+<p>With global patching, you can:</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Task</p></th>
+<th class="head"><p>Action</p></th>
+<th class="head"><p>Note</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Patch all supported algorithms</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
+</pre></div>
+</div>
+</td>
+<td><p>If you run the global patching command several times with different parameters, then only the last configuration is applied.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Patch selected algorithms</p></td>
+<td><p>Use <code class="docutils literal notranslate"><span class="pre">--algorithm</span></code> or <code class="docutils literal notranslate"><span class="pre">-a</span></code> keys with a list of algorithms to patch. For example, to patch only <code class="docutils literal notranslate"><span class="pre">SVC</span></code> and <code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> estimators, run</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>Enable global patching via code</p></td>
+<td><p>Use the <code class="docutils literal notranslate"><span class="pre">patch_sklearn</span></code> function with the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">sklearn</span>
+</pre></div>
+</div>
+</td>
+<td><p>After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Disable patching notifications</p></td>
+<td><p>Use <code class="docutils literal notranslate"><span class="pre">--no-verbose</span></code> or <code class="docutils literal notranslate"><span class="pre">-nv</span></code> keys:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span> <span class="o">-</span><span class="n">nv</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>Disable global patching</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">unpatch_sklearn</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p>Disable global patching via code</p></td>
+<td><p>Use the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument in the <code class="docutils literal notranslate"><span class="pre">unpatch_sklearn</span></code> function</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+<span class="n">unpatch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>If you clone an environment with enabled global patching, it will already be applied in the new environment.</p>
+</div>
+</section>
+<section id="unpatching">
+<h3>Unpatching<a class="headerlink" href="#unpatching" title="Link to this heading"></a></h3>
+<p>To undo the patch (also called <cite>unpatching</cite>) is to return scikit-learn to original implementation and
+replace patched algorithms with the stock scikit-learn algorithms.</p>
+<p>To unpatch successfully, you must reimport the scikit-learn package:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sklearnex</span><span class="o">.</span><span class="n">unpatch_sklearn</span><span class="p">()</span>
+<span class="c1"># Re-import scikit-learn algorithms after the unpatch</span>
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading"></a></h2>
+<nav class="contents local" id="contents">
+<ul class="simple">
+<li><p><a class="reference internal" href="#install-from-pypi" id="id6">Install from PyPI</a></p></li>
+<li><p><a class="reference internal" href="#install-from-anaconda-cloud" id="id7">Install from Anaconda* Cloud</a></p></li>
+<li><p><a class="reference internal" href="#build-from-sources" id="id8">Build from Sources</a></p></li>
+<li><p><a class="reference internal" href="#install-intel-r-ai-tools" id="id9">Install Intel*(R) AI Tools</a></p></li>
+</ul>
+</nav>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>To prevent version conflicts, we recommend creating and activating a new environment for Intel® Extension for Scikit-learn*.</p>
+</div>
+<section id="install-from-pypi">
+<h3><a class="toc-backref" href="#id6" role="doc-backlink">Install from PyPI</a><a class="headerlink" href="#install-from-pypi" title="Link to this heading"></a></h3>
+<p>Recommended by default.</p>
+<p>To install Intel® Extension for Scikit-learn*, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
+</pre></div>
+</div>
+<p><strong>Supported Configurations</strong></p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="install-from-anaconda-cloud">
+<h3><a class="toc-backref" href="#id7" role="doc-backlink">Install from Anaconda* Cloud</a><a class="headerlink" href="#install-from-anaconda-cloud" title="Link to this heading"></a></h3>
+<p>To prevent version conflicts, we recommend installing <cite>scikit-learn-intelex</cite> into a new conda environment.</p>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-0-0-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-0-0-0" name="0-0" role="tab" tabindex="0">Conda-Forge channel</button><button aria-controls="panel-0-0-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-1" name="0-1" role="tab" tabindex="-1">Intel channel</button><button aria-controls="panel-0-0-2" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-2" name="0-2" role="tab" tabindex="-1">Main channel</button></div><div aria-labelledby="tab-0-0-0" class="sphinx-tabs-panel" id="panel-0-0-0" name="0-0" role="tabpanel" tabindex="0"><p>Recommended by default.</p>
+<p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id3">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id3" title="Link to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div><div aria-labelledby="tab-0-0-1" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-1" name="0-1" role="tabpanel" tabindex="0"><p>Recommended for the Intel® Distribution for Python users.</p>
+<p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">software</span><span class="o">.</span><span class="n">repos</span><span class="o">.</span><span class="n">intel</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">python</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id4">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id4" title="Link to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div><div aria-labelledby="tab-0-0-2" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-2" name="0-2" role="tabpanel" tabindex="0"><p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id5">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id5" title="Link to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div></div>
+</section>
+<section id="build-from-sources">
+<h3><a class="toc-backref" href="#id8" role="doc-backlink">Build from Sources</a><a class="headerlink" href="#build-from-sources" title="Link to this heading"></a></h3>
+<p>See <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md">Installation instructions</a> to build Intel® Extension for Scikit-learn* from the sources.</p>
+</section>
+<section id="install-intel-r-ai-tools">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Install Intel*(R) AI Tools</a><a class="headerlink" href="#install-intel-r-ai-tools" title="Link to this heading"></a></h3>
+<p>Download the Intel AI Tools <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html">here</a>. The extension is already included.</p>
+</section>
+</section>
+<section id="release-notes">
+<h2>Release Notes<a class="headerlink" href="#release-notes" title="Link to this heading"></a></h2>
+<p>See the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/releases">Release Notes</a> for each version of Intel® Extension for Scikit-learn*.</p>
+</section>
+<section id="system-requirements">
+<h2>System Requirements<a class="headerlink" href="#system-requirements" title="Link to this heading"></a></h2>
+<section id="hardware-requirements">
+<h3>Hardware Requirements<a class="headerlink" href="#hardware-requirements" title="Link to this heading"></a></h3>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-1-1-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-1-1-0" name="1-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-1-1-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-1-1-1" name="1-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-1-1-0" class="sphinx-tabs-panel" id="panel-1-1-0" name="1-0" role="tabpanel" tabindex="0"><p>All processors with <code class="docutils literal notranslate"><span class="pre">x86</span></code> architecture with at least one of the following instruction sets:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>SSE2</p></li>
+<li><p>SSE4.2</p></li>
+<li><p>AVX2</p></li>
+<li><p>AVX512</p></li>
+</ul>
+</div></blockquote>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>ARM* architecture is not supported.</p>
+</div>
+</div><div aria-labelledby="tab-1-1-1" class="sphinx-tabs-panel" hidden="true" id="panel-1-1-1" name="1-1" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>All Intel® integrated and discrete GPUs</p></li>
+<li><p>Intel® GPU drivers</p></li>
+</ul>
+</div></div>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our <a class="reference internal" href="blogs.html#blogs"><span class="std std-ref">blogs</span></a>.</p>
+</div>
+</section>
+<section id="software-requirements">
+<h3>Software Requirements<a class="headerlink" href="#software-requirements" title="Link to this heading"></a></h3>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-2-2-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-2-2-0" name="2-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-2-2-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-2-2-1" name="2-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-2-2-0" class="sphinx-tabs-panel" id="panel-2-2-0" name="2-0" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
+<li><p>Windows* OS 10 or newer</p></li>
+<li><p>Windows* Server 2019 or newer</p></li>
+</ul>
+</div><div aria-labelledby="tab-2-2-1" class="sphinx-tabs-panel" hidden="true" id="panel-2-2-1" name="2-1" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
+<li><p>Windows* OS 10 or newer</p></li>
+<li><p>Windows* Server 2019 or newer</p></li>
+</ul>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<p>If you use accelerators, refer to <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html">oneAPI DPC++/C++ Compiler System Requirements</a>.</p>
+</div>
+</div></div>
+<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:</p>
+<ul class="simple">
+<li><p>1.0.X</p></li>
+<li><p>1.1.X</p></li>
+<li><p>1.2.X</p></li>
+<li><p>1.3.X</p></li>
+</ul>
+</section>
+<section id="memory-requirements">
+<h3>Memory Requirements<a class="headerlink" href="#memory-requirements" title="Link to this heading"></a></h3>
+<p>By default, algorithms in Intel® Extension for Scikit-learn* run in the multi-thread mode. This mode uses all available threads.
+Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Single-thread mode</p></th>
+<th class="head"><p>Multi-thread mode</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>SVM</p></td>
+<td><p>Both Scikit-learn and Intel® Extension for Scikit-learn* consume approximately the same amount of RAM.</p></td>
+<td><p>In Intel® Extension for Scikit-learn*, an algorithm with <code class="docutils literal notranslate"><span class="pre">N</span></code> threads consumes <code class="docutils literal notranslate"><span class="pre">N</span></code> times more RAM.</p></td>
+</tr>
+</tbody>
+</table>
+<p>In all Intel® Extension for Scikit-learn* algorithms with GPU support, computations run on device memory.
+The device memory must be large enough to store a copy of the entire dataset.
+You may also require additional device memory for internal arrays that are used in computation.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="samples.html#samples"><span class="std std-ref">Samples</span></a></p>
+</div>
+</section>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="index.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn*" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+<html class="writer-html5" lang="en" data-content_root="./">
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="samples/knn_mnist.html" />
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+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
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+  <section id="samples">
+<span id="id1"></span><h1>Samples<a class="headerlink" href="#samples" title="Link to this heading"></a></h1>
+<p>The following samples are also provided as Jupyter notebooks in Intel® Extension for Scikit-learn* repository.
+If you want to run them locally, refer to <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks">these instructions</a>.</p>
+<p class="rubric">Classification Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+</ul>
+</div>
+<p class="rubric">Regression Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+</ul>
+</div>
+<p class="rubric">Clustering Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+</ul>
+</div>
+<p class="rubric">Dimensionality Reduction Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a></li>
+</ul>
+</div>
+<p class="rubric">Jupyter Notebook Samples</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p>There are also <a class="reference internal" href="kaggle.html#kaggle"><span class="std std-ref">Kaggle kernels</span></a> that use Intel® Extension for Scikit-learn* for a variety of machine learning scenarios.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Explore the complete list of oneAPI code samples in the <a class="reference external" href="https://oneapi-src.github.io/oneAPI-samples/">oneAPI Samples Catalog</a>. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.</p>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/ElasticNet.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-ElasticNet-for-Airlines-DepDelay-dataset">
+<h1>Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-ElasticNet-for-Airlines-DepDelay-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Airlines_DepDelay_10M&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Let’s encode categorical features with LabelEncoder</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;UniqueCarrier&quot;</span><span class="p">,</span> <span class="s2">&quot;Origin&quot;</span><span class="p">,</span> <span class="s2">&quot;Dest&quot;</span><span class="p">]:</span>
+    <span class="n">le</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+    <span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((9000000, 9), (1000000, 9), (9000000,), (1000000,))
+</pre></div></div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_train</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">ElasticNet</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="mf">0.3</span><span class="p">,</span>
+    <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s2">&quot;l1_ratio&quot;</span><span class="p">:</span> <span class="mf">0.7</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">ElasticNet</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.28 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 1.0109113399224974&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class ElasticNet</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">ElasticNet</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">ElasticNet</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 3.96 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the ElasticNet algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 1.0109113399545733&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="svc_adult.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn SVC for Adult dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+   
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+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
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+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/2025.1/samples/ElasticNet.ipynb b/2025.1/samples/ElasticNet.ipynb
new file mode 100644
index 0000000000..29e5dca851
--- /dev/null
+++ b/2025.1/samples/ElasticNet.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/daal4py_data_science.html b/2025.1/samples/daal4py_data_science.html
new file mode 100644
index 0000000000..ae70e62be3
--- /dev/null
+++ b/2025.1/samples/daal4py_data_science.html
@@ -0,0 +1,921 @@
+
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+  
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
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+
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+        </div>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Utilizing daal4py in Data Science Workflows</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/daal4py_data_science.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
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+           <div itemprop="articleBody">
+             
+  <section id="Utilizing-daal4py-in-Data-Science-Workflows">
+<h1>Utilizing daal4py in Data Science Workflows<a class="headerlink" href="#Utilizing-daal4py-in-Data-Science-Workflows" title="Link to this heading"></a></h1>
+<p>The notebook below has been made to demonstrate daal4py in a data science context. It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user’s Power output in the absence of a power meter.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">glob</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+
+<span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="n">sys</span><span class="o">.</span><span class="n">version</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;3.8.10 (default, May 19 2021, 18:05:58) \n[GCC 7.3.0]&#39;
+</pre></div></div>
+</div>
+<p>This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage. Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">workout_data_dd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/cycling_dataset.csv&quot;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>altitude</th>
+      <th>cadence</th>
+      <th>distance</th>
+      <th>hr</th>
+      <th>latitude</th>
+      <th>longitude</th>
+      <th>power</th>
+      <th>speed</th>
+      <th>time</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>185.800003</td>
+      <td>51</td>
+      <td>3.46</td>
+      <td>81</td>
+      <td>30.313309</td>
+      <td>-97.732711</td>
+      <td>45</td>
+      <td>3.459</td>
+      <td>2016-10-20T22:01:26.000Z</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>185.800003</td>
+      <td>68</td>
+      <td>7.17</td>
+      <td>82</td>
+      <td>30.313277</td>
+      <td>-97.732715</td>
+      <td>0</td>
+      <td>3.710</td>
+      <td>2016-10-20T22:01:27.000Z</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>186.399994</td>
+      <td>38</td>
+      <td>11.04</td>
+      <td>82</td>
+      <td>30.313243</td>
+      <td>-97.732717</td>
+      <td>42</td>
+      <td>3.874</td>
+      <td>2016-10-20T22:01:28.000Z</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>186.800003</td>
+      <td>38</td>
+      <td>15.18</td>
+      <td>83</td>
+      <td>30.313212</td>
+      <td>-97.732720</td>
+      <td>5</td>
+      <td>4.135</td>
+      <td>2016-10-20T22:01:29.000Z</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>186.600006</td>
+      <td>38</td>
+      <td>19.43</td>
+      <td>83</td>
+      <td>30.313172</td>
+      <td>-97.732723</td>
+      <td>1</td>
+      <td>4.250</td>
+      <td>2016-10-20T22:01:30.000Z</td>
+    </tr>
+  </tbody>
+</table>
+</div></div>
+</div>
+<div class="line-block">
+<div class="line">The data above has several key features that would be of great use here.</div>
+<div class="line">- Altitude can affect performance, so it might be a useful feature.</div>
+<div class="line">- Cadence is the revolutions per minute of the crank, and may have possible influence.</div>
+<div class="line">- Heart Rate is a measure of the body’s workout strain, and would have a high possibly of influence. - Distance may have a loose correlation as it is highly route dependent, but might be possible. - Speed has possible correlations as it ties directly into power.</div>
+</div>
+<section id="Explore-and-visualize-some-of-the-data">
+<h2>Explore and visualize some of the data<a class="headerlink" href="#Explore-and-visualize-some-of-the-data" title="Link to this heading"></a></h2>
+<p>In general, we are trying to predict on the ‘power’ in Watts to see if we can generate a model that can predict one’s power output without the usage of a cycling power meter. Below are some basic scatterplots as we explore the data. Scatterplots are great for looking for patterns and correlation in the data itself. Below, we can see that cadence and speed are positively correlated.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;cadence&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;hr&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;cadence&quot;</span><span class="p">,</span> <span class="s2">&quot;speed&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;speed&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;altitude&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">workout_data_dd</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;distance&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_0.png" src="../_images/samples_daal4py_data_science_7_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_1.png" src="../_images/samples_daal4py_data_science_7_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_2.png" src="../_images/samples_daal4py_data_science_7_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_3.png" src="../_images/samples_daal4py_data_science_7_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_4.png" src="../_images/samples_daal4py_data_science_7_4.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_7_5.png" src="../_images/samples_daal4py_data_science_7_5.png" />
+</div>
+</div>
+</section>
+<section id="Using-daal4py-for-Machine-Learning-tasks">
+<h2>Using daal4py for Machine Learning tasks<a class="headerlink" href="#Using-daal4py-for-Machine-Learning-tasks" title="Link to this heading"></a></h2>
+<p>In the sections below, we will be using daal4py directly. After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py’s training and prediction classes to generate a workable model.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">daal4py</span> <span class="k">as</span> <span class="nn">d4p</span>
+</pre></div>
+</div>
+</div>
+<p>It is now the time to split the dataset into train and test sets. This is demonstrated below.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">workout_data_dd</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">train_set</span> <span class="o">=</span> <span class="n">workout_data_dd</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3000</span><span class="p">]</span>
+<span class="n">test_set</span> <span class="o">=</span> <span class="n">workout_data_dd</span><span class="p">[</span><span class="mi">3000</span><span class="p">:]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">train_set</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_set</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(3902, 9)
+(3000, 9) (902, 9)
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Reduce the dataset, create X.  We drop the target, and other non-essential features.</span>
+<span class="n">reduced_dataset</span> <span class="o">=</span> <span class="n">train_set</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="c1"># Get the target, create Y</span>
+<span class="n">target</span> <span class="o">=</span> <span class="n">train_set</span><span class="o">.</span><span class="n">power</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="c1"># This is essentially doing np.array(dataset.power.values, ndmin=2).T</span>
+<span class="c1"># as it needs to force a 2 dimensional array as we only have 1 target</span>
+</pre></div>
+</div>
+</div>
+<p>X is 5 features by 3k rows, Y is 3k rows by 1 column</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">reduced_dataset</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(3000, 5) (3000, 1)
+</pre></div></div>
+</div>
+</section>
+<section id="Training-the-model">
+<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Link to this heading"></a></h2>
+<p>Create the Linear Regression Model, and train the model with the data. We utilize daal4py’s linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d4p_lm</span> <span class="o">=</span> <span class="n">d4p</span><span class="o">.</span><span class="n">linear_regression_training</span><span class="p">(</span><span class="n">interceptFlag</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">lm_trained</span> <span class="o">=</span> <span class="n">d4p_lm</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">reduced_dataset</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model has this number of features: &quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">NumberOfFeatures</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Model has this number of features:  5
+</pre></div></div>
+</div>
+</section>
+<section id="Prediction-(inference)-with-the-trained-model">
+<h2>Prediction (inference) with the trained model<a class="headerlink" href="#Prediction-(inference)-with-the-trained-model" title="Link to this heading"></a></h2>
+<p>Now that the model is trained, we can test it with the test part of the dataset. We drop the same features to match that of the trained model, and put it into daal4py’s linear_regression_prediction class.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">subset</span> <span class="o">=</span> <span class="n">test_set</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;power&quot;</span><span class="p">,</span> <span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Now we can create the Prediction object and use the reduced dataset for prediction. The class’s arguments use the independent data and the trained model from above as the parameters.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lm_predictor_component</span> <span class="o">=</span> <span class="n">d4p</span><span class="o">.</span><span class="n">linear_regression_prediction</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">lm_predictor_component</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">subset</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">prediction</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">300</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">test_set</span><span class="o">.</span><span class="n">power</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">300</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_daal4py_data_science_24_0.png" src="../_images/samples_daal4py_data_science_24_0.png" />
+</div>
+</div>
+<p>The graph above shows the Orange (predicted) result over the Blue (original data). This data is notoriously sparse in features leading to a difficult to predict target!</p>
+</section>
+<section id="Model-properties">
+<h2>Model properties<a class="headerlink" href="#Model-properties" title="Link to this heading"></a></h2>
+<p>Another aspect of the model is the trained model’s properties, which are explored below.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Betas:&quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">Beta</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of betas:&quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">NumberOfBetas</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of Features:&quot;</span><span class="p">,</span> <span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">NumberOfFeatures</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03
+   8.53155955e-01 -1.09595022e+01]]
+Number of betas: 6
+Number of Features: 5
+</pre></div></div>
+</div>
+</section>
+<section id="Additional-metrics">
+<h2>Additional metrics<a class="headerlink" href="#Additional-metrics" title="Link to this heading"></a></h2>
+<p>We can generate metrics on the independent data with daal4py’s low_order_moments() class.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metrics_processor</span> <span class="o">=</span> <span class="n">d4p</span><span class="o">.</span><span class="n">low_order_moments</span><span class="p">()</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">metrics_processor</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">reduced_dataset</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+<span class="n">data</span><span class="o">.</span><span class="n">standardDeviation</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,
+        1.81623064e+00]])
+</pre></div></div>
+</div>
+</section>
+<section id="Migrating-the-trained-model-for-inference-on-external-systems">
+<h2>Migrating the trained model for inference on external systems<a class="headerlink" href="#Migrating-the-trained-model-for-inference-on-external-systems" title="Link to this heading"></a></h2>
+<p>Occasionally one may need to migrate the trained model to another system for inference only–this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;trained_model2.pickle&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">model_pi</span><span class="p">:</span>
+    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">lm_trained</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">model_pi</span><span class="p">)</span>
+    <span class="n">model_pi</span><span class="o">.</span><span class="n">close</span>
+</pre></div>
+</div>
+</div>
+<p>The trained model file above can be moved to an inference-only or embedded system. This is useful if the training is extreamly heavy or computed-limited.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;trained_model2.pickle&quot;</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">model_import</span><span class="p">:</span>
+    <span class="n">lm_import</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">model_import</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>The imported model from file is now usable again. We can check the betas from the model to ensure that the trained model is present.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lm_import</span><span class="o">.</span><span class="n">Beta</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,
+         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])
+</pre></div></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f04b5fa7650>
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+        </div>
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+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/2025.1/samples/daal4py_data_science.ipynb b/2025.1/samples/daal4py_data_science.ipynb
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/2025.1/samples/daal4py_data_science.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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gRPNPNpFsnKcqAI8E+L1y1MvuNbbCteEHgOunnoFfvrzLUa8/UUgm9u+cPnYofrR6u+k1KgT2fXoSN/xhS/Q6L55Vj3HDqmyvD2s1FR6l+J5lPeFLRIMAXArgDAA1ACqI6Bqb191ARFuIaMvhw4eTOkdtVVlS4+nAWPqXrbp6pzLOlrbjfSoldDP/ZBLJsfMMKQKyJGHBhWc7lmG6we8hNNRUgeAc3ok3p1+/shtf/PmruOqRt3Dez17Bf730D82zn1VvSsbe/OUxuHtti+k6L1m/I+GD0Ugy1z9bG/IKceNfpijF8ttchH2+BOBDIcRhACCiPwH4IoAnjC8SQiwHsBwAGhsbk/LZP+22rwl0Gu8rRq82qChQBRBSRMY9CCfvGxApV/i49YCSSSTbzdMnSxg9pAJhmzp7N/hkwhWNw3HLyiZTmabpNR4Ji2fXRz/AxpDYQkN5p276/uuVVjz4aiu8HgnfPX80BLTUwX0v7kpKX78vFVZOoYd0ryRLLcSRiHysiss0uTD+ewGcR0T9oIV9ZgLYks4THDlhL93sNN4XbMXHYshUXb3TDdtQU5VyhU8y4Ry3FSvx5jl/+lm4/6Vdbv9kAIC+l+vpd/bFDRF987yR0dh8UFFx3ZRRaKipMhn+WBQBKCEVv3q1Ne4c4hmGVCusnB68J3rCWLJ+R9oMdSmGOBKR6aq4fCQXMf/NRLQKwLsAwgDeQ8TDTxdOad1MpHudNNiNJON1u/Xu9Nc6JR9TLSW0M9RBRUFHdxDtnQGT4qZ+7PHDB8Y9ZrwP1lWTRmDZhlbXtfxAxEC7yAs8uvFDCCD63vz2rx+4Pkc83OjrO13/eO+xUyXU3WtbEEzjSrKvG/+KtSKm1Mpvc1LtI4S4E8CduTh3urEzlh4JkCUJPtm9B5HMMjz2tU7Jx1RKCWMNdXcoDFUANz75XnReAkg6ZGD3wdKNyC0XjsG9f9mJJOy/KzKR3/dIhOd/cL4rmWX9+rcePIENOw/h064gHnh5l+N1s10hKQJeWTJVSPV1JZmJsFSxUErltyRcbG7JNY2NjWLLFveRoVG3rXf82Z6fz0rHlEysaTpg8WqT8SDaOwOY8otXTRUrZV4JmxbNsK0kcfvavtB68AQ2th7BT5//uyne7fdoOQVj2MQnuzeIOk++9RHuXrsDgEBQEfBKgEPBTl4hE/D2HV8CALS0dQAgNNQMcLz2P37ufTz+VmxfAA279y32XtJXdul+v+3u2URGPFv3HpM+iGirEKLR7mdFqe2TbZyWi26X0Bt2HoIco9/v5N2lU6vHafke1bgnsiQ6ZYkim6d6PdGgInDxgxtx31yzATG2X+wKKlHP8pE3PsDDMSEYo+H3ewhhRUAI7cHSk0cd2DyyhEff+ACPbvwwmm/wSMAD8yZY/vY3/9nuaPgB+/fN7l7q7/ekPRadSogjX3WickWhh7/Y+KcJp+WiU0/Y2kHl2Nh6BIue3QaPZN2c5LQMT1dVQryqEj0ZaIcmemY1xsGwaopF68cHgJ6QGmnCov1evPCOBET2ARACikAgjww/AATCqiV3EFaBW1c2Y2A/HxpqBkTf10SLaqf3LfZeykQsOhXDVYoVMU4UQ/iLjX8GceoJG1sSaqTCJ0MRwtG7S0dVgl21x4JVzRjYzws41Mz388pQoc0L0IydU/kjAMsDxK0RVwGohtfml+l3JqgIfPcPW6AKQFHVhPkLnwzT+5aqFEQqRly/Lz2StrK785J6XD1pZMLfi733goqKGy+oc3XOfCEd3nqxVEux8U8Tdh5+7A2i94R1qgyq8Mu4+5IGTB87NKVKErfYLd8DYYF/f+JdKKqwrby5d+45mHxmdfRc9acPwMUPbkTQ8FrdC3RTAVWMdCeRtPjPiz8T9RTjeZGtB0+gad8xTBg+0JJTScX7tFvZ3fHn7YAArj4v8QNAv/ee3LwXD23YjeWvf4CHXmtN2fPNZugkXd56sYS/2PinAbubamR1RdIGUFFFQsOv05eqBKcmJicddO/9MmH4Kf1M56s7tT/um3sOFqzSxNAU1bxasTs+08vUOq1LWDwv8lcv7zLlDK6dPAL3XPpZx99bsGpbNPTkdG/sP9oNj43s9d1rW3DRuNNc31O/ea0VgbBAIBw2zTmZe9KtMc6Ut+7metmRzfBXJh+ORannn02cJBYqfDK6Q+52FPtlyoimu9P2faOefD9vYnkFksj2xhb6/0Xvd8bja5VBxYUMwNNHafBrJ4+IevF2sgIyEZ55+yNLsvjxN/ei9eAJxyKBQFjFv/9ha1xJkdpB5bYy2V7ZvZRBOqQQ3HaYS5dUit2c3VwvO7LVjyHTMjFxPX8imhjv50KId9M6mwLEaQnY1tEDIoKbqLUgwvr5U5MqlUxEIq9KX763tB3Hdx7fYgr1eGWCRFrzEqecgv7h1Uo+tRWDnjdoqKnClLrB+MGMOvzqld2uNfl1vjV5JH7/5kep//EZREHE+KeQjPDLBAGgceQp0bEKn4yAYvYiu4IKfv3KP22P8dimPfjTe/ttiwQARBvRO3ni1ZV+3HlJvRbqMf5dwn1v4nR4vm5CJ+mMrTuudhNcLycyvSEsG3mFRGGf+yP/lgFoBNAMLSN4DjRJhslpmUUB4/RBAATKPDJCSmLv3y87txpMhWT0eRpqBmDm2KF4fvsn0fErvzAcN80cE/fGbmnrgGTxPLW8QTCsQICi7RCduPGCMyBLMoZW+vDR0W6MHlyBCxtOQ0tbh63xT9Hmpp1UN6PpSW/9vdCrghTFesDYB4LOqq37LA/Tcq9kyTcYPfHY9/HqSSMBoYV6vLIUt8DAjnQUHbh5gKQztm6cs0RkCXGmctxMbgjLRl4hrvEXQkwHouJrE4UQ70e+HwfgrrTMoMAx3lQyEUKKtuO2oaYKQYcPcCzpjhe6vXE0gbNtlgTvii37cdPMMY6yDbHCaEZOGqSZE/HIGx/C7/FYViYNNVXwymZNf48E/GhWPe5Zu6Pg08heSUJLW0fcklo7Zo07Da/vPoKgwaGo8MtYcOEY/OyFnab3I6Sq2H6gA1csf9N29Xf1eSNx0bjTUvZc3Xi+8eLVbh4g6Y6t9652OyKrXfP1yqeS1WzkFdwGZc/WDT8ACCG2A/hM2mZR4MyZMAyLZ9UjpAr4PBKWrN+BTa1HMH+6uzK4eY21afUgnPR5jDdOb9jGanzixW/N4Z7UIQBBBbbx3upKP+6/fDz8Hgn9fDL8HgkPzJuAS8bXwOfN3zxChU9GmVfCtZNHwO+R4PdI8MkEr2xeIWnvDVli0Hb4ZMJdl9Tj5R9Owz1fG2d5XxVV4JLxw7B07nhTDHrxrHosWb8jbky9urJvPaPj/b6bePWcCcOwadEMPHH9JGxaNMOS7M1EbL260o9pY4Zarle+ibhlI6/gttpnGxE9il7Z5asBbEvbLAqc9s4AlqzfgWBYRTDilC18dhvWzZ+KZRt2JzSUuqft9MYmm/HXb5ybVzRFQxSqADa1Hol+wOKVY8bzMOx+r9wjQRFwtdLxSITvTjsDv//bHnQFnVcmTp7lvM/Vxt01m0v+44Iz8fUvjEB1pT8aNtt+oAN3rumNr3tlwuLZ9dBkLZxDfcb9HkajOK+xNloyrH9vJ+TnJBKXjXLEZOLViUIn6Yqtx36GCkHELdNzdGv8/w3A9wDcFPn+dQC/TetMChinMEtXUMHSueOx0LDTNTacob/WTQesZGqTp9QNhixJUb38kCJMH8DaQeW21Uh+T3zFSq1axGy0BAF3XtKrLtodCjvG/MOqwH+//oElbq4/cNo7AybNHD309Ny7+/Cn99qwsfVIwr89V1T4PRZpjyuWv2n6W4UQuGdtC3yyHO1oJpO2m7kssqpZPLse42qsQn3tnQGs2LLfdE6j4xBrSGNXCV0BBdsPdCRUYe0r6Y5X9zW27vQZKgQRt0zO0ZXxF0L0ENHDAJ4XQvwjIzMpYOLF58YPHxh9elf4ZLR1dFvijYGwYtvRqi8Z//1Hu+GTJXMVT4IPoEcirP9+fIG2ja1HTO0wPRKiH6aLGnpjyADw5j/b8cMVTZaHnV3CdPGsemxsPYJbDKsVCcCVXxiB57e34ejJzDTiSSfDY1ZLdkYwrGoPQL0+3u+R8Mi1jaipKovqH8Wr0XdrVKsr/Vg821rVs2T9jqTq+VMh2S5vmfS+i2U3biZwFUAlojkAmgD8JfL9BCJak8F5FRSJ4nN6bLTu1P6meKM/EguWJMLsZRstcdG+1FMn+gA+uXmvxQiXe+W4VUftnQHcurLZZMyJCFPqBpv+Tt1bmT2+Bvdfrv2t/RK0a9x98AR++EyTaU4qgCff3lsQhh8Avvfku6b30Km80IhPllBV7kXdqf0Txt+TTQKOq6myOBXZaE3oNl6djXanpdie0S1uwz53AvgCgNcAQAjRRERnZGpShUgy8bk5E4ZF5REAEa34iPVI+pLxj1dN0d4ZwEMbrJ2qYpPCsbS0dVi8+JAi0NLWgWljhjr+rU4VFkbypa7/rCEV2H24K6XfDcaE1vT3YMGqZsikheBUIUwPuGQqOJItsawdVB4V00vlfH0h0echWx45i9E549b4h4QQHWSu686Hkuu8IpH4llHauCuowC9LJm0c4xI+2qkrUrWRbDvA/Ue7MaVuMDYtmmH5ANqFhABg/vSzEhzbfmvrB4e70FATcPxdY4XFwkhde75q9zsZfp8suUpoq4pqeg/3tJ8EQNEPy6zPno4Xtn+SUn094N7J6Mv9ky7ixauzpY+Tjj0JbihEeWe3xr+FiK4CIBPRWQB+AOBvmZtW8aA1LWkBoHmGxqSek0di6dTlkAC0w02C2M4b8nskfHXcaWjedyzqFcXezDVVZZBJa6No5L4X/4Gf/2VnwmS0vuLRmsTsdL0PIh+YdlY1Xt55OOHrgiqw+YN27Gnvst0Lsbr5Y/gkIKQK3HrhGIysrjC1x3RDoiRgX+6fbJFNjzzTVTOFKu/sqpNXpNn6HQAujAz9L4D/J4ToyeDcouR7Jy8nnnzrI9zx3HbbnxlrsWM7gKXaLSmZTkuxnZyMctM9YQVCCJR7ezdg6a0bAThuTEo0T31zmEwSAmEFkkSRev/CXESWeSXba6HLY7jZC1HplxFWzSWdffEi2zsD+OLPXzWt6uJ1hUumx3C6SaWbWL6R793N+tzJSwhxEsAdRPSTyNeMDcYPDgDcvW6H42tlieD3SFg3f6qpyqN537GUl8PJLKWN3lCFT8bsZRtN8VdA24AFaNr9ACX01O3CVkaJ695KHi2prCoCHrmPKmk5wkvA3Im1eGKzdc8BgaC6VDXtDJi1ZXTJh0T9mZ14cvNeSzjPaXe3fp6gomD+9LNwSoXP4oxk0hgXQq19IgpZ3tmV8SeiLwJ4FEAlgBFENB7Ad4UQ/5HJyRUSsUu/f/viKMSza10BBXeuaYnGffXa674sh5P9XT18YPfAMaJ55om9WKew1b2XnYOB/XyW6iIBazObQiEkgK5AyPZnsUldN8RKPujvxR3PbbddHdihJfJ3W8aDivkesEu23v/SrujPs1kSWQi19vEo5ISy273yvwTwFQDtACCEaAYwLVOTKjTs5Gl/+9cPbJt7GGWOu4KKrbRBvDI5o0xzrGRzslvC9d+v8Ml91t/3yRTt8mUn1dt2tPgWjGuaP7Yd/2xN/2huxy3xJB86A9b7xA4tkW8tqZ0/vc50D9iVP9rBJZGJyYYMQ6Zw3cxFCLEvptonZRlKIhoIbSUxDpoDeJ0Q4s1Uj5dr3HSu8skSbv3KGJzSz4c717SY6undShsYPeqesAJVFfB7ZChCxdK54zFnwjDXS2n9WLoY3dfOrcFz77WlFH+XJeD5H2ibw+xWETIRWlMsn8xrHGRGm/Yfhy9OLwOvRFCFgCSRpvwaWR011AyI+xBOFE6w80J9MjB+eJUpqexm/wGQu41ZhUahhq/cuif7IqEfQUReIroVwN/7cN5fAfiLEGIsgPF9PFbOqR1Ujp6w87Own1fGo99sxA3TzsT0sUMttddBRUFHdzCu6Fbs6iKkCChC0yMPhAVuXtFkWgHE2zBkPFZXUEFQEVix5QD+44Iz4xotnW99cYRJdO2X8yZEdwXbGZauoIKn386POv5UcIreOaVABIDrpoxCmVeyXQGEVO29kwh46Opzo6JmRi/Sbsd3QFFtx3VivVCPBAgQbnzyPdMmKv11foeuNLpAXa42ZhUiiT5z+Yhb4//vAG4EMAxAG4AJke+ThoiqoIWMfgcAQoigEOJYKsfKJ+JVTakQaKgZAMD+A6oKWD6gsew/2g0RRx8/rAItbccdf24MEe0/2m3pAgUAv/nrB7hzdn10bn4PQY5p+eeVCd+fMQZ/u20Gnv7OefjbbWY1RicDZhRx08nXVK9XAm6aURdNRqeSlWioGYBNi2Zg+Tc+B59D8scny6gq95kMxpwJw7Bu/lTcPacBE0dUmV4fCqu2O8GN6EqZD109EbIkIaSIaPhtwaresNGcCcPwt9tm4pYvj4Hf0xuy+Mm/jsNT3zkPmxbNwJS6waYQ4+u7DmPhqsTdt5jCwG21zxFoSp7p4AwAhwH8f5HE8VYANwkhTHEBIroBwA0AMGLEiDSdOjPsP9qNcq8nWh2jU+6VIACLB2XXRUtv+qIn2fTj6stIreNTIjMkIsJoxwEINNRU4WhXEI9t2oNVW/dGtfMXz65HyMZt9cqEccOqTBvDNrUeMfXpXTrXLFsRS3tnACOrK7Bu/lQ07TuGO57bbisbrc02/5AJeOGmaegKKnh044cIx1HejMeJnjA27DykyXh4ZJMGv45dWMUYjouV2hBA1ODaJWKN4Ziqcq9lI18grOKpzXvx/ZlnAdDev+/PPAtXTRoRN8TYHQqDSMtHuKkkKhRKPXzlttpnNLRQzXnQ7sE3AfxQCPFBiuecCOD7QojNRPQrALcBWGx8kRBiOYDlgFbnn8J5soZTDFVRBe6c0+BYoXHoeA88EsHoN3klCU9u3ovfvNZqaQjvVFcOaIZ736fduO737zhWmugGaMm6Hbj1wrPx0xd2mn4eVgQ6ukNRQToguXim0WAEwmFcWH+ao+HPVwSAnZ8cx9jTBtg+IN1yu0FQzaZfOvweKZogN26sc9PgJVHppl4ialeau2zDblw1aYTpd2MrbuyqgQCBkM2DsFAqW2Ip1I1Z6cRt2OcpACsAnA6gBsBKAE+neM79APYLITZHvl8F7WFQsOihjthwbFARWLJuh2NT6rvWtli8u6Ci4KENrbYN4e0o92pNQ+66pAH3rGtxVWLolSRMGl2Nn/zrOPhkQrlXhlcmKKqKG598F1/8+St48JXdrnMIgDUnEVSAde9/4vj6fEUVwPynm/DVX7+Br51bk7Zj+mSKhtJu+bIWNhOAKX7+1Oa9rqpwYnNEdtVmS9bvwHVTRll+1yfL0Qqe2GoxHTfVQP3i5AXyHbfN44sdt9U+/YQQfzB8/wQRLUjlhEKIT4hoHxGdHZGHngnAeTdUgaBVwtuXfzg1pTaiN++48YI6LH/9A4sUc1dQMWmUBBUV100ZhclnDkZDzYBIHF+CmyIs3Vvb094FLfQvovX2eujq/pd2YdmGViyd684jclPxVEiEFIHVTR/jP786Fkv/9x8IJehHnIhLxtfg2smjTJveYr3rZRt2w00mJKxqOSLjqtBuo9HkM6vx2KYPbdsVxvN8E1UD+T2Eh6+ZiIaaqoIz/EBhb8xKJ249/xeI6DYiGkVEI4loIYDniegUIjolhfN+H8CTRLQNWvL4pykcI2/QP8h2G5Z6QuYKDTuvqsIv4+45Ddi0aAaumjTCcdOInsz7zrTRAASeeGsvbvjDFmxqPRJRcExseH0yTPX4gbCw3Y8AaDHiBau24fVdhx29onTuFcg3ZInQE1YhSVp5JgDbtoxuWN3UZhHXi70PfLKMz5xm7qVwzrABlvMpqrCsCu3umYaaKtt2hYD9Xgyn/SIeSQsr6sdYOnc8po0ZWrCGspA3ZqUTt57/vMi/30Wva0sAvh75fnQyJxVCNAGw1ZsoROJ5vX7ZnLizu/EUVWD62N4PUyIVwt+81opAuLchyMJnt2HTohlYOnc8bnqmyTGROmvcabjna+MAABt2HrKt+IklEFbx73/YChUCN39Z26cwIdKbQPcePRIhqAh8bUIN/vzu/rxV7EwWRRV4yKYN59PXT8KVj25OandyWDVLX9vdB4Gwgqb9HaaxbQeOo8InOZ5LXxW6be/oVkIk9vcAq9BfoZItpc98x63xXwStLv84ES2GFqNfIoR4N3NTKxziLZNJIpNHUV3pt/ShNTZwN1bL2HV2irdknVI3GD6PuSLDJxP+8+LPYGrdYIvBjte4xcjJkPa6nz7fmyCe1zgMa5o/NoWvVmzZDwlagrOPUZKc45UJl00chtVNbQgYQmkeScL2tuPa5iybCp749D5s7QzQZefW4sm3rVpB8aq8Qqq2skymvaNbzzf294rJOBbqxqx04jbs86OI4Z8KYAa03bncwzeCcZmsd+fSN/jEehTtnQGs2Gr9oLZ3BkwbaGYv24iP2rssN2W8D66u02/E75Fx7ohBqDu1vynOrAuKAb2ben7yr+Oidd+JOm+t2HIAkk18WkXhGn6ZgGVXnovv/ctoSASsaW6zPCBPhhT87IWdtv2PE6Hv9dDRw3hPXD8JmxbNwL/ZJGgBYOFXznYMwdx72TnoCipJdasqZEmCdFKIG7PSiVvPX/8EzALwiBBiPRH9vwzNqSCJVcl06sfq5LnbiXrZ1XPrH1xj7b3xgxv7YAgqKjq6Q9Ga5thzV/hl3H1JgynsdNWkEQk7b+nHLgYImkTF3XPGYexp/XHziiYEFYGAw67tQFiFVyb4Pfr37p52R7uClvvB6F1XV/px7eQRpvDNtZNH4IZpZ+KyibWOIZj2zkBcT96unp09X8at8T9ARP8N4MsAfkFEfrhfNZQMbhQKtc1a1g+qLuoVq4mzYechk2EG9KSLAIS5uig2lNATVqLlm3rtd6J8g34cY+ctgkB3yGrgrpsyEsvf2JPosuQ9AloFzeLV2yERIRyzdCnzSoAAegzhtJAi8K3JI/HEZveyFU37jkVlMJy459LP4trzRqFp37FobgWIH4KJF8OOV9WT6H4t9U1QxU4yCd+LANwnhDhGRKcDSKnUs5TRP4gUkYLQdV+cRL26ggruWtuCH63eHv3Q6qEbzds0a8EbE3xRz13pLd9csn4HFs+ux5J1vZrti2fXR8MDdpr/J3rCuHvdDngkmIzil8YORc3AcvhkKthmLLGoQpNjjiUUVuGRrb7OE5s/gleyPiycGFXdz9XrBlX4cNap/TGowhcdi+0V4caT70ufXN4EVfwk08zlT4bvPwZgr2fL2GJX36+qIqqGCfRW+Ri39sc2+7AL3cSuEKor/agq98Eny9GKIEALL42r6ZVv2H6gw/QgMD5g9PDVkvU7TH2GdV7eeQgv7zyUkWuVb0gS4bopo/Dbv5o3tIdVuDb8HgnweuLnUQB7o6t3UTNKLRjVQJ08+VTr2bPVXF0/Vy67iZUyriWdmdTQtXY+OHwCnph9/n6PjLaO7mh+QPfeNuw8hLvWtpiSsvqH1kk1M3aFUDuo3JKU7A6FTR+oK5a/afqA37qyGR939OCXL+/SJBoU1XVHqmKGQGioqerTKscjSwnryO2M7oJV2wAIBMIiRmrBrAVlZyRTrWfP1iaoRA86XnFkFjb+GWR10wFD60IrPWEF33l8C3yy2YubPnYofrTa3PtXl/M1xnfjrRAAQOu/0GusjP0Y7D7gQUXgZxG9n2LZqZssdg3qg4qKm555r09VTItn1yc0nLarOokiuR375HM8o5xqPXs2NkHZP+i0dqGBcOZXHAwb/4zR3hnAwlXNtoa/wi8jrAgoqoqAgmhoZsGqbRjYz4eGmgGWTTvhsIpZD74RbdqSaIUAaA1kjGJcZR45aijcNvQoFWSJcM+lDagq8+CmPzZZtPr7mtYYnmIbTkWN30IzkVFOpaonlYdGsqEa+/ClZFG3KEXZhWzBxj9DOGntlHsl3H1JA4YOKMONT75rkoHWd9MqQnswGFGBaNMW3ROyWyHoxuAv2z+x1KgbDYX+Ab9lZXPB9tFNJ8uvmYjOoIJbVjY7NmmxwysDALm4hol3UzsZXQDRMbuYfyLDqOcCdCkONwY6VTVXt6Ea2wedUCOrnF5KUXYhW7DxzxAVPtlWAlcVwPSx2hZ/O89b303rhN60ZdqYIXGNxZL1Vq28OeNPt/QVkIgw/+n3XP9dElB0ASGJgO6QYqiicke5V8KPZ9eb5Jvt8EjWDV5OOBndvkotpGKg3ZQup5ocdvOgK1XZhWzBxj8DRBtyyJLJwHtlMjVD0W9+iQgnXUotaPQaKDtj8fquQyAbT/O599qw6KLPmD5Mk8+sjpRxujszFbj1L/fK6I55wKoC+OGKJssV88laGadTrF8VAoGwCp8EGBuVeSLRC6/c21/ZrpIFsDfidka3L1ILmaze6Uty2M2Djg1/5mDjn2bsSjq9MuGX8yZg8pnVFs9br8mPbcIiSwSZBGKfCV5ZqzwxYjQMq5sOYOGqZlsP1itbP5TVlX48MG8CFqxqhkwSwqoat6Jl/vQ6/OqVVlfXIl/Qk7jeiIdvh90wEcEjWd+Dfj4ZwbACVQD3vfgPxHao9MgSnrjuC9jTftK0UWt10wEsWNkMIoqG9cq9Hosnnu5SR7sWoEIVaYml9zU57OZBx2QGNv5pQv/AdnSHLJ5QmUfG8FP6OVZkxBpzAIAQeOCKc9HS1oHHNu2xbaNoN4d4oQtFCNsP5ZwJw1B/+gA07TuGMq+MW1c123aTmtdYi6H9y5wuQd6iXw2bTcpxufLzw/HsuwdMLRgrfDIWfOVs/OyFnZH2m2aNJEUIzGusxTWPvW0KXUypG4wf/rEpsoronYie89E98Y2tR9Je6mjXAjSgiLjN4N3CCpmFCxv/NGCMpwYV1ZKsTaSzYtcDWBGaQVCFwI9n12PcsKqEnqCTtHSZhwAixw9lovkDgE8CnntvP559d7/lZ/kMQfP8UynTHDW4n9WrVVQMrvRb+uPqGkkThg/E7GUbLSGWO2fXx52Dpu90PCPhma6gYmkBWuaVXKu6JoJ1ggqTotTnqfQlN94XYlvCBcIqiAh+D1kUE42qnV/8+avRVolOZZcng0q0JZ+bD5XTccIqcPOXxpjCCnr7Prv52xmpoAoEFSRVCZNLPJIWs7/94rEp9xcIhoVFrVWSCLesbEJPjOibrpHUFVRsQywfHOmKey7tfRNJqXO6xSkE09cqGuN9VOoKmYVIUXr+wWBy433Bztsu88j42f8Zh56QGo352iXdtFaJu7F07vje5C/IUvFjl0CzW0HoS/BbVzab4vZhVeCnL+xEhd+DyjKPKaxw4wV1lvkXqiSzjkzAj2bVY2rdYLR19MAruynFtPLAy7uwadEMrJs/FRc/uBGAiHrPHklrwu6TzaGOo11B2xDLeWecgkfe+NByjn5eCSp0faeqjGyuykRohrV/Cp+iNP6hJMf7gp233R0K45aV20yGwa7PKqDV7uuduDYtmoGWtuMRKWWDgmSMAYj3wdO2BNkburvWbodEkmkH5bINrRAu2j8WEooAfv6XnQgr2iqsL/sYdK/bL0smjaNyrwcPXT0RVeVe0wPYKcQyuH+ZRa5ZIuB7F9ThqkkjLBVg6YyfJ2oQlMrxsqX9w2SOogz7OH3UM+HQGhtjVPhleGUCkbZFPVGfVR2jZz9tzBAsnevcaCM2TKMfv/XgCby+6zAWrGy2rVwBAA9JmlyAAZ8sYWR1RZ+ugUSat51P9IRUhFXENfy+BJPW+y87VbQ01AzA+OEDASAa/ogXYrlp5hjTOVUBPPSauXIqtsFLX71pNw2CksWu/3A6wlNMdilKzz/bRKWP17ZAJjLpvgO9fVb1JiyBcPylfbwEmlNS9+JfvwEhgFCcmI0iVChhs8HrDinYfSh+PDoRqgAuahiKv7QUlsrn3M/V4k/vHdBE7MIKVCFM5bZ6/+W6U5PTy3d6bfO+Y/B7ZFP1kF1IL12ljpny0LkBenHAxj8FYuPt7Z0BTfpYsddh0T8Y44cPxJS6wXhq814s27DbJOjmttbZ7oNnV5ZppJ9Xq+IRoIhWTC+x36dKoRl+ALhuyhm45cKzo/LVF/3qddPP9dJYY9ikraMHgEBDTZWjcdVDeLEP72wbzUypc3J5Z3FQlMbfQ4BdqbsnDaEJO09vZHUFZLIevJ9PhirMbRarK/34/syzcNWkESnpmMd+8HTZZacdut+7YDQmjx5sySPoFHhu1xFZq26N6uB0BcOmRPakMwahK6hgUIVWB7+x9YjlGESEv2z/BEvW77DV1bFLluvG1a7yJRNGM949k8mHDZd3Fj45M/5EJAPYAuCAEGJ2Oo/tFObtq36Zk6f3wy+NsdRM+z2Eh6+ZiIaaKsfNXcbxZKonjJuyBvXz4tuPb7V9nU8mXD91dLSxu53xL1ZkScJT138BJ0MqXv37Qfz+TXO7xc0fHsXVj76FroDi+AD0eSTcvbYFQcVeS3/ZhlbEPj4zobLpRKJ7JtMeOu/ELWxy6fnfBODvANwpXiVBphK+dstoCcC9f9lpee2PZzdg2pihCZUU9WYvCyO5ALvYbKx3Z/zQBxTVVoNe/3s3tR7BlLrBJSffrAoVVz66GR6J0O0QFjNKYdsRUgS8soSgjUAfoCXLb5g2Gg+91pqUcU2H0XQbz2cPnXEiJ8afiGoBzALwEwA352IOqWC3jD7pYFiGn9IvoWem/1yKNLAwoocPYrf73/ylMbjvpV0IGh4UToSU3jJSu/r/YsBJZ067nKJPZZ5fm1CDNc1tjj8PqSqumjTCNoSXaZKJ57OHztiRq1LP/wKwEHH0IYnoBiLaQkRbDh8+nLWJxaO60o/Fs+pdvfZ4d8i2JLO9MwDA7LnZyTiHVK3MMPYYP31hp6WnbryKRSGADTsP4URPOKXkrt8j5V0Zp5FMrmfWNLdh8ez6aNmtR9KE9WJLcHOxu5Urbpi+knXPn4hmAzgkhNhKRBc4vU4IsRzAcgBobGxMympV+iR0xkotRsb7yrhhVaj0y3FDBl6ZMKDcG9czcyrZNCaJu4KK7WtiiefcBsIqFj+3Hd0pxvsVVcUfbzgPV/3ubdtG7n3F3Ggys/g9BAGKevReSUJnIOx4/tiG96lq6fcFp4QuV9wwfSUXYZ8pAOYQ0cUAygAMIKInhBDXpOsE08YMxvPbraWH08YM7vOxaweVIxzjQXskLcFoVN6sqSpDIGztpFXhk9G875jtpq/YJHF7ZyAtsfpUDT+gVbycMaQSX/+8ua1kukhk+Ms8WqI6mQeELuRm/B2JgEeu/TwaagagutKPRReNxf6j3dh+oAN3r90OwNqcXfek+6Kl3xcShQ05ns/0hawbfyHE7QBuB4CI539rOg0/AKgOTrnTeDI4eVy6Lj9A2PfpScxethGSpGVi/TKBJMK8xlrMXrYx+nvzGmuxYst+03GmjRlqey5ZInQlSFBmAkUReObtvXjirfQb/kTMa6zF7HNqHMtUY/FIBFkiCAGoMQp0EiFq+IFeA37F8jcjev29hr/MK0HElOhmG7cJXY7nM6lSlHX+r+2y33DkNJ4sdh5XtHsXkaXsUwXw8JXn4san3zN9mFds2Z9Qb8V4ru0HOrBk/Q4IVSCgCPg9lFTbwVRQASx9cVdGz+HEmuY2zD7ndHhlgkHtGv28MibXnYJX/t6bC5JIE7CLXZXplHs9aGk7btLicQq9ac+B3CU62jsD2LDzkGXvCDczZ9JJTo2/EOI1AK+l+7g9Dg6y03gqGD0uu+5dRkKKwHeffBexrTN02QddHybRucYPH4hJZ5yCpn3HMKq6H/a0n8Rda1sSlizGwysTLjt3GJ7Zkn86/V5Jwt/+2W75+3rCCu69bDyOdgXRtO8YwoqK2xL00e0OhfGdx7eYxPacSmB1eY5ciJXpToRHsjoRnNBl0klRCrtlGzuhq1jCirBI/RpzAHoVUDxWNx3A7GUbcffaHbjmsbfxaVewz0lYVRVYuTU3hv+Kxlr442y7DioqfvfGB5ZxVQBHu4KoO7U/5jYOR82gfra/r1fm+D32YnsAoqJ8/bzWrlaZEisz6uDHjutOhPGBV+GTLQJ/DNNXijLsky7c9lJ1aqISS5lXgqoK+CPyALE5gHi7eu1iwD99YWdUJdInWxOWbshl2f+sc2owpW4wbl7RZBFUI4lw4wV1WPZqK+wKOpv2HYv2xm2oGWBpQu+RgBd+cD66ggo6uoO48cn3ojtzgV7D3ttHObGUdjzc3ivxkrh2YSi9Q9j0sUPZ8DNphY1/DPqHWI+vxyZ1E5Xd2cX8dYQQeGDeBAwo96Kmqsy23V/96QPQFVRQ4ZNNuQC7JtwAogZfFcB/fnUslr74jz5tbMomNVVlmDZmCE70hHHX2hZ4JK2x+fwZmvYRADz4qn2+YYIhVBbbhF4RKpbOHR99ONhVTRkNu1FK2650MpFhdyvNkSiJa+dE6B3C2PAz6YaNvwFjvFVfdusf0h/+sQmSRPDJEhQhsHh2PcbV9PbVNSVm2zpwz9odEEILXfhlgiIEVAHc/qf3HUXBhCpw8YMbIZGm1Kl7wPdedg7qTx9gCRsZCasCY0/vj7dun4mWtg6sfu8Ann3PeXdqtondiavLJeuKqCGldzfuQ6+1Rhuc3Hf5BNz0TJOpbPPaySOihl0nXtmj25r4eIl8J8OejGxyol25XLvPZJOiNP4DyyQc67GGCgaWOcflEyVtFaGVPYYiOi93/Hk7KnwylEhJ4JwJw6If4D3tXQAEPJE8wHVTz8Bjm/YgEFbjioIFYiShA4oAIhINy7/xOUuHKCuE6ko/aqrKsWbbx7av8EgEVRUZ3Rlr5MrP1+KyibW46tHNprAUSYTaQeW2KxqhiqhB1A3ym/9sx5HOHkytG2Ix/Drxyh7d1sQ7JfKdDHsyMgtuduVy7T6TLYoy4evUZDxe83E3SdtYuiIN1u1kGwJhgZMhBUEF+N3GD+GVrR205k8/KyodEE9CwStJON4djttbVyYtjLK66QAu/vUbtqEfjwSoQmStitErE8YNG4irf/c29JP6ZTIlLyt8sm3P2wpfbwK2utKP2eNr8K0pox0NvxuSlWFw07EqGZkFY9c3uy5tqc6TYVKhKD3/EzbSDvHGAfdJWzsSyTZoypDWSh9dFExPNioOlTtaT+BmkNCOUeaVoKgCqiogS1qiV5IIsx7ciLCiOiZxs63ovOArZ+OutS2mB5EigL/Mnxptat+07xj8HrPctE+W0NbR3SdDnw7cGPZkQzXs2TP5QlEafw+AsMO4E7po2x3Pxa8Xt8NoEGyTdkLgzkvqsWTdDlsDUVXutdXb90qAJBFUAdPPVFXghR+cDwD4SqT7VMihi1iu6OeTcfhEj2UFElYF2jq60fLxcSxc1WyraBpUVHzn8S1YOnd8n3vY9oW+5AoSHZeNPpNritL4Ozm4iRxfN6JtHpnQOHwg3tpzNDo2r7HWJBsQazAWz9KSw067ee0eGH6PhEeubQQgLGWKfo8cLWGMF8rKJSeDCv4Q00BF53h3GLdEyzvtH1iBsMjJJqtYUskVMEwhwMbfgJ1omxGfR8JT3/4CrnnsbdP4ii37cdPMMdEPv50kQ/RBMLtXEjreA+Pey85BQ80AtLR1WJqJ6CuNju5Qgr8oM0gR4bRE9NhIT8gEnOgJuQpB5YucARt2phgpSuOfKkYjDJgbo3tlwn1zz4HXI7uq7jAKhxmrReyqhACrh7mx9Qim/OJVeCUJqtCSteVejyn0YLe5KRuoApj5mSEYM7Q/Hnnjg+j5ZQK+/vkReK7pgGWvgz5Pj0T48Rp3oTW7xKne+Uxvop5Oo+x2oxbDFANs/GPQ++Ne/Os3TOMSAVPqNEloJ088FifhMN0wxoY1dA/TrsTQ75Hw0NUTLcqUD8ybgB/+sSnrO3Vf+fth3H7RZ3D9+aOjaqYNNVpHzmffs8pF6FGteHsV9MSvcX9DbJ/jWwy7gb0y4f7L05MXSKaHMsMUA0Vp/Ct8hK6g1chU+NzVOHYFFfg9MoKGOLtPltHSdhxN+46ZQh4eCY7VHbWDytETjtP0xVA2aPQ47R4aPllCVbnXcp4pdYPhkSXHSqFM0rTvGOY2DjfJUAOwTZzHzs4nASoIXllLaP94dj3GDauy7GzWae8MYOGqZtMqJ6QILFjV97xAMhu1GKZYKErj7yEJgNXoauOJse3VGwzj+v95x1KyKUtSdEVgRA9PqHGC4yFVxfYDHbhi+ZsWGYnYh0ZPWHFeXcRIHmeLMq+M9s6AxUC6SZwHVaCfV/ubF1x4Nq4+b2Tcc+0/2g3Z5n2VJYqbF3ATykm0UYvDQUwxUpTG38nbjueFG9Fj/7esbI6WKuo7fGPxydZ4v7Exu12Uo59XhgqBxbPqsWT9DovHuW7+VMtDQwj7h8j2Ax22RrbMQ7YJ11RwEo1b9Ow2S+4CsE+ce2WCRIAnImMNINq7+Kcv7ESF3xP3AVA7qByKsK5uFFU4iq+5DeXEq+fncBBTrBTlDl/HcvckbOGUusGQXESJYuP9iRqz+z0SHv7G57Bp0QyMG1Zlu4P0sU17LA+Ncq/HIi/cevAE7l7bYjmHV6K0JYHLvBJumnmW7Q5kpx3O+492mxqfl3kl3H/5ePzttpm4e06Dafeuzt1rW+LKWldX+rF07nh4DJfLKxOWzrUPuRnfB6OMs905nHbeAnB9DIYpNIrS868s8yBw0hoHqSxz/+fuP9oNovjtxf0e6/Z8N43Zp40ZEh2P9TiDiopVW60tE4OK+SGzuukAFqzaZuuRh9zUYbpEUQV+9cruuAllPUSysfVI1EsOKgqum3IGJp852JSknj52KP7TZiOd12YFFYtRfjlRtU8ymjvGYxvDO837jiV1DIYpJIrS86/w2Rt5p3H718qOImoeCbjly2Pwt9tmWEIAdiEEn0xY+JWzsW7+VNPrqyv9WDy7Hj6PhAq/1rBj/vQ6+D3Wec6fXmcRHOtrIxc7ZDI2QdF62SbqExBUFOz79CQWrmqOesmBsMBv//oBvvP4FmxqPRJ9bXWlH3deUm85hiKcwzdGdPnlaWPiyxwno7ljPLZRUyeVYzBMoVCUxv9odzCpcTu6ggr8DmprskRRyWEdvTsTAFMIQatmEbj/xV2YvWwj1jQdiP7O6qYDEckHQiis7QS+atIIm92+FNW3B1IToXPLH284D2/dPhNPXD8Jj1zbiHKv8wOzwidHxOK0XIVdP+FA2BoquXrSSPzka+PgkyljXarciqglOsbiWfUZnSfD5IqiDPt45Vj1eOO4O2oHlYMksm115ZNl09LfLim4adEMtLR1aN2hFOBEpBxHLyEEYJGQXrJ+By4ad1pCPZm+iNDFwysRvB7ZtN/A7jx+D+HHlzRg+KB+0e5XISVxSavxb7j6vJG4aNxpGa2i6auI2uqmA1iyfgd8Hk2Y785L6jnZyxQNRen5l9n0Y403bofuOdr1mDUu/Z0SiwBQVe6DTzafUzeELW3HIcVoKxtbC66bPxV3XlJvCRUZ51bmlVDmTf4t9HskU+JUR5K0B4vTKsbvkSLhrpm4etLIqCBdIuJJHGdaujjVc8T20w2GVSxZt4OTvUzRUJSe/7GT9uEdp3EndM/xqc17sWxDK3xyrycOAM37jqGjO+SYFHSKGW8/0IF71u2wqFnGKy/UPVh9E9SUusFYN3+qdScyALJfsETxyRLOHzsUz2//xDR+xeeHm5K2xlWM0XvWHw4VPtlWkO66KaPw2KY9puuVLgOfrZr7ZBPGDFNoZN34E9FwAI8DOBVaKc1yIcSv0nkOn0w4GbIL1yTfxaS60o/vz9R6ytrp7gQVFYpDUtBJ4XPJeqvh93vIUl6oG55bVjZDIoCITO0db7ygzrITWQVQJhOUODX+QUXBKzsPWcafeXsv/vjOfgTC5n0HmxbNwPhIz9zYB9O8xlqs2LLfUgd//fmj026ks1lzz8leptjJhecfBnCLEOJdIuoPYCsRvSSE2JGuEww7pR+OtXXajqdKPN0dr0zwe7RcQKynGxt3tvMo+/lkPHzNREwbM9S2vLBXE1/7V2/vuGzDbti15TJu7ir3SgirAkKIqDDcjRfU4bd//afl9wgSZMk+FOX0t6/Yst9WqjrdSpjZlmDgfrpMsZN14y+E+BjAx5GvTxDR3wEMA5A24y+RvYfvNJ4Mdsa7zCPjoavPRVW5z9bTjTWEsR6lKrSadSC5ZK5PlnHDtNF46LVWSEQ4GaOkWeGXcfclDZg+dmh07rWDynG0K4gHX91tOZ6ACkU1x/CN3q5TKKQrqERXBpkiF2EY7rrFFDM5TfgS0SgA5wLYbPOzG4hoCxFtOXz4cFLHdSqDTEd5pFM4oKGmylViMVEJYuzPnZKz+nmvmjQCmxbNwMPXTLQkpxVVYPrYodGHz/jhA7Gx9QhmL9toWS94JOC+yyfgisZa07ixUU0uQyG5Ojf302WKlZwlfImoEsCzAP6vEOJ47M+FEMsBLAeAxsbGpLasdjjU8zuNxyM2wZiOcEAijzL255taj5h6DNhJHk8bMxRL546POy9j6ETHKxN+OW8CJp9ZDQDR8+gYG9XkMhTCYRiGSS85Mf5E5IVm+J8UQvwp3cf/+FhPUuNOGBOMQUXF/Ol1uGrSiLSEAxLFxI0/N57PSfI49nV2P3cKWQ0/pZ9rOYNchkI4DMMw6SMX1T4E4HcA/i6EeCAT5/B5CSdtql18Xvcxf7sE4/0v7cKyDbujjcWzaXzcJlDjvS5R6MRtaCWXbQ25pSLDpIdcxPynAPgGgBlE1BT57+J0niBoU+YZb9wOJwkFvbF4IW72STbfwHIGDFO85KLaZyPs6hPTyOkDy/DPI922426JV3VTyJt9ks03FOLfyDBMYopS3mHk4Mqkxu3olXewXqJC3+yTqIKFK1wYpvgpSuNfN8TeyDuNOzFnwjD87bYZuOXLY+D3EIdCGIYpGopS26fGIbzjNB4PO3kHNvwMwxQ6RWn8hw+yl3FwGndDIVaZcONxhmGcKErjf/RkKKnxYiSbImhuHzLtnQFXLRgZhsk8RWn8R1Xbe/hO48VGNkXQ3D5kVjcdwC0rmqKN5b0y4f7Lx3NzFIbJEUWZ8H1xx8GkxosNuz0KenlqOnFqZBO7B6K9M4CFq5phVLEOKQILVhXmfgmGKQaK0vi/89GnSY0XG9kSQXP7kNl/tBsyWW81WaK0P5AYhnFHURr/aRGRMrfjxUa2duq6fcjUDiqHIqwb5hRVFPR+CYYpZIoy5j+82r6e32m8GMnGTl23SpvVlX4snTseN8fE/JfO5f0SDJMritL4T3BoLOI0XqxkozzV7UNGfx1X+zBMflCUxr/u1P64dvIIPP7m3ujYtZNHoO7U/jmcVfGSjOLotDFDsjAjhmESUZTGHwDuufSzuPa8UWjadwwThg9kw88wDGOgaI0/oK0A2OgzDMNYKcpqH4ZhGCY+bPwZhmFKEDb+DMMwJQgbf4ZhmBKEjT/DMEwJwsafYRimBGHjzzAMU4Kw8WcYhilBcmL8iegiIvoHEbUS0W2ZOs/lv3kDZ96+Hpf/5o1MnSKttHcG0LzvWF5o3OfTXBiGST9Z3+FLRDKAhwB8GcB+AO8Q0RohxI50nmfUbeujX7+z9zhG3bYee34+K52nSCvZbLtYSHNhGCYz5MLz/wKAViHEB0KIIIBnAFyazhM4efr5ugJw2xGr1ObCMEzmyIXxHwZgn+H7/ZExE0R0AxFtIaIthw8fTuoE7+47ntR4rslW28VCmwvDMJkjbxO+QojlQohGIUTjkCHJyQBPHD4gqfFck622i4U2F4ZhMkcujP8BAMMN39dGxtLGyv84P6nxXJOttouFNheGYTIHCSGye0IiD4BdAGZCM/rvALhKCNHi9DuNjY1iy5YtSZ/r8t+8gXf3HcfE4QPy1vAbae8MZLTtYqHOhWGY1CCirUKIRrufZb3aRwgRJqL5AP4XgAzgsXiGvy8UgsE3ko22i27Jp7kwDJN+ctLMRQjxPIDnc3FuhmEYJo8TvgzDMEzmYOPPMAxTgrDxZxiGKUHY+DMMw5QgWS/1TAUiOgzgoxR/fTCAI2mcTjrhuaVGvs4tX+cF8NxSpdDnNlIIYbtLtiCMf18goi1Oda65hueWGvk6t3ydF8BzS5VinhuHfRiGYUoQNv4MwzAlSCkY/+W5nkAceG6pka9zy9d5ATy3VCnauRV9zJ9hGIaxUgqeP8MwDBMDG3+GYZgSpKiNf7YaxbuYx3Ai2kBEO4iohYhuiozfRUQHiKgp8t/FOZrfHiJ6PzKHLZGxU4joJSLaHfl3UA7mdbbh2jQR0XEi+r+5um5E9BgRHSKi7YYx2+tEGr+O3HvbiGhiDua2lIh2Rs7/ZyIaGBkfRUTdhuv3cA7m5vgeEtHtkev2DyL6Spbn9UfDnPYQUVNkPNvXzMlmpO9+E0IU5X/Q5KL/CWA0AB+AZgD1OZrL6QAmRr7uD62fQT2AuwDcmgfXag+AwTFj9wK4LfL1bQB+kQfv5ycARubqugGYBmAigO2JrhOAiwG8AIAAnAdgcw7mdiEAT+TrXxjmNsr4uhxdN9v3MPK5aAbgB3BG5DMsZ2teMT+/H8CPc3TNnGxG2u63Yvb8M94o3i1CiI+FEO9Gvj4B4O+w6VucZ1wK4H8iX/8PgK/lbioAtOY//xRCpLrTu88IIV4H8GnMsNN1uhTA40LjLQADiej0bM5NCPGiECIc+fYtaF3zso7DdXPiUgDPCCECQogPAbRC+yxndV5ERADmAXg6E+dORBybkbb7rZiNv6tG8dmGiEYBOBfA5sjQ/Mgy7bFchFYiCAAvEtFWIrohMnaqEOLjyNefADg1N1OL8nWYP4j5cN0A5+uUb/ffddA8Q50ziOg9IvorEeWq65Hde5gv1+18AAeFELsNYzm5ZjE2I233WzEb/7yDiCoBPAvg/wohjgP4LYAzAUwA8DG0ZWYumCqEmAjgqwBuJKJpxh8KbV2Zs5pgIvIBmANgZWQoX66biVxfJyeI6A4AYQBPRoY+BjBCCHEugJsBPEVEA7I8rbx8Dw1cCbOzkZNrZmMzovT1fitm45/xRvHJQEReaG/ik0KIPwGAEOKgEEIRQqgAHkGGlreJEEIciPx7CMCfI/M4qC8bI/8eysXcInwVwLtCiINA/ly3CE7XKS/uPyL6FoDZAK6OGAtEQirtka+3Qourj8nmvOK8hzm/bqT1Gf8/AP6oj+XimtnZDKTxfitm4/8OgLOI6IyI5/h1AGtyMZFI/PB3AP4uhHjAMG6Myf0rgO2xv5uFuVUQUX/9a2hJwu3QrtU3Iy/7JoDV2Z6bAZMXlg/XzYDTdVoD4NpIFcZ5ADoMy/WsQEQXAVgIYI4Q4qRhfAgRyZGvRwM4C8AHWZ6b03u4BsDXichPRGdE5vZ2NucG4EsAdgoh9usD2b5mTjYD6bzfspW9zsV/0DLgu6A9pe/I4TymQluebQPQFPnvYgB/APB+ZHwNgNNzMLfR0KormgG06NcJQDWAVwDsBvAygFNydO0qALQDqDKM5eS6QXsAfQwgBC2m+m2n6wSt6uKhyL33PoDGHMytFVocWL/nHo689rLIe90E4F0Al+Rgbo7vIYA7ItftHwC+ms15RcZ/D+DfY16b7WvmZDPSdr+xvAPDMEwJUsxhH4ZhGMYBNv4MwzAlCBt/hmGYEoSNP8MwTAnCxp9hGKYEYePPMC6JqDwOzvU8GCYdsPFnGIYpQdj4MyUHEV0bERRrJqI/ENElRLQ5Itr1MhGdGnldNRG9GNFTfxTaRhr9GNcQ0dsRbff/Nuz+7CSin0SO/ZbhWKeSpqnfHPnvi/GOwzCZho0/U1IQUQOAHwGYIYQYD+AmABsBnCc00a5noEkiAMCdADYKIRqgaR6NiBzjMwCuADBFCDEBgALg6sjvVAB4K3Ls1wF8JzL+awB/jYxPBNCS4DgMk1E8uZ4Aw2SZGQBWCiGOAIAQ4lMi+iyAP0b0ZnwAPoy8dho0gS8IIdYT0dHI+EwAnwPwjibBgnL0CmwFAayLfL0VwJcN5702ciwFQAcRfSPOcRgmo7DxZxjgQQAPCCHWENEF0LpMxYMA/I8Q4nabn4VEr2aKgvifsXjHYZiMwmEfptR4FcDlRFQNaD1RAVShV/72m4bXvg7gqsjrvgpAbzjyCoC5RDRUPwYRjUxw3lcAfC/yepmIqlI8DsOkBTb+TEkhhGgB8BMAfyWiZgAPQPP0VxLRVgBHDC+/G8A0ImqBFv7ZGznGDmh5gxeJaBuAl6D1XI3HTQCmE9H70MJB9Skeh2HSAqt6MgzDlCDs+TMMw5QgbPwZhmFKEDb+DMMwJQgbf4ZhmBKEjT/DMEwJwsafYRimBGHjzzAMU4L8/4LABxPmbYcxAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ygMcYY/e3Q3uySi6kG3CyWVj18+a21hxqwvxVtTZddbL61ngrEQAgifCaQ+4jp0GyR742W7auMjwyYf70CiOhni7weDaFXBwE4k0oYkUxZ3sikquY7xnvHk0a3jfLLey8uHJJJaJNjLHL2qE9CZEJl9T2xK2/tRs3vLqjp3HdU+sRDCfn6mhti9X9dtZYe3xCLIMoz313Qlkfm4urzyPh9bsnoqxfd257Opr3kX4PAbieXAi/+/iIe5ReYrmkuhUKzwIYCGA5gBZ9O2Ps/9LVyGToyELBrb+1U6wAb7BPNo7CqS16DMGp1hB65HtRUpSX0ADNe5EzHeuRKRIZlLqKLz3vnojBu2OQjjiFPAANACabtjEAWRUKHZVEYhIScVW16mJbggoamgMxX85YbVlfdwL3L69GKKKWkglYMGOE6yymPLVJLqjo4mEd2BIZ5N08284wcFrvyUPXl6OhJZj2QMpkV1+C5HElFBhj/57phnQlEhnoefr5oKKgqTXIHfCLC/0JpVFwrsPchAdWbDUEAgAoDFpWU0JUQZ1EyeWMoObBLqgo+PaEc/H8hr2uA6biPdvOsIrgCT5ztls9liDVwDLzvWoNhUFEyPPIHfa+dRRcuaQS0XAiepuIaiKfRxLRf2W2aZ2XRNxfrS56HglQGXDX0o+5JSydMnU6+b4712EmyBJxj3nolZpO6UtvvXeBMMOz7+52DJjiEevZdpZqc/HiZHRSca+13quwqrlau+nTgtRwG6fwBwA/AhACAMbYVgDfyFSjOgMNzQGs23GcmzY50Syq00cPxIZ5k/HM7EsgS1LUy2EdVGoPNUGxxALoUc5O3HVlGfye6LZUlPSwnUdHZcDGXQ1ub0WHwe1gF8ttNNazzXaAZLpw450GpOZeG+9ZBMIMv/zXDiEYMoBbm0I3xtgHRFEzx3AG2tMpWFl1EPctqzICvrwy4fuTz8fN4wYnXW2ruNCPonwvfLIUNXO1qibmLq8CJ/ATT63ZGfX7ejv15TnAcOekYVH7PDzVXgRH50QneBmtHk4FPjnmYNfNJxvV5mI9L6dnm+0AyXThJk7G70ktsMyN4Fn6wX6s+Kgei2YKVVI6cSsUThDRedCMyyCimQAOZ6xVHZiG5gAeWFENs8YhpDA8+eYOPL12JxbNHGV0YF23rufWjycc3KgmOO8nAMBjsVnw9MLPvFOHm8cNNo4ZMbAI3bwyN73AxLI+ru6HlVzxWLHmYNJjIWaNLcVLHx6w5ZWKFzBlvQae3aQzxSVY42QWrt5m2GHmXHW+0Y/c9Gsz5vtovldngmHw4h4DKQRpCvi4FQp3AVgM4EIiOghgD4DZGWtVB6a+sRUySQDsA2kgzGwdOBHDY6xBpfrAyZhRyAqLnpG6MXaX9sqHwuznk4nQq8Dn+p7o2DxWppajoTn9HivxMAtEHf3vZZX1eO3uK/CPmiN42tIup4CpRJ5hLntfJSqcdcE3alBPXFPRP2mPLR3eMRvmTTZWc9c/td5m3wGSC9LsDB5gmcKt99FuAFOIqACAxBhLLdtaJ8ZpINUxd+BE02UDiakmdDwSsGjmKFdRx2bBUVzox5yr7Jk+u/nkpF5Cm8fK39PvseKGWGk89JxFd1+tzXbjDRxun6G1boRbt972IlWvKPPKKJl+7XTMhnmTjXvlpM4MKomp4DqDB1gmcet9tIuIlgK4FcDgePt3ZYoL/Vg0cxQ8DnfWPPAma3jUZ2fWlN5mA6ffQ/jel4ZhybcvxeYfT7F1erfG7pvHDU5LCc728FhxSywBejYcNrKX8u6zFTfPcGXVQYz72Vu47fkPcNvzH+Kyn7+ddLLCTJBur6hE+3VDcwBrtx+Dx+LtZj2GV9MbAOZcVZaQeqozeIBlErfqo3IA4wBcAWAREV0AYCtj7OsZa1mWSMeyUp/N1x46hY27TuD5DXvgk2WbDtnNbD2R9iSqmnCzvybkUteDt4fHilvMajhzXQcACCnAV3+zDgumj8Dsy+LHYsR7hk42prkrckcPnkjcjBsSMajrs3aewdp6DK+mt1cCrh3R33XbnASTqMnQhluhoEBzR1UAqACORf7rVKRrWakP5BUlPTBp+Dm444ph3IE3nuExmfYkGhjmZv906MGjPFYkQksg/R4riWAW3NYKaYraFqRn1ZVbsT7DoKLgrivLjO+dbEyyRDkzEKXbK8qtQZ1n2wGAAr8MRbV7efGEuSxLmPr0etfvaoFPtv3e2ZAaVduiq+M299EZAJ8A+CWAtxhjOeGkns7cR4nkGIpFMgO5k0dOOtqTa+jXunTTXizb0qZCuX5Efyz42oh2v7bqAyfxzcWbuB5WMgEeWeIawa3PzKle9oSyPrj8F29zvJkkvP9g7jzLTOSkild/u/rASdzy3GacDrR5txf4ZMyfXhGzNCkv+aNPBp771hfjeoc1tQbxnRcqo1aHfpmw7LuX55ydJ5OkI/fRNwFMBPCfAO4govcBrGOMvZ2mNmaddCyhEzGw8VwYre2xFqRhKsuZ2WWyGInxtkZ7NL/92TEsyEJ7YjkGKAxQwqrNCM5LIzKhrA9+904dApb9N8ybjEUzR+FeS9zKopm55YqaCa8o830yu6rGUp8qjMWtVd0SVOCXpSihEFSAb//vh5CI8Mi0iijVnzV1ifVpk0QdLlYkk7j1PloJYCURXQjgWgA/BPAAgE5zJ9OxhHYrWNysJrT6yJbIZIVldZlbd/Q0qg6cxOhBPW2prhNh464GWMfhbNXa1R0D7nmpiusHb0bLCXWKK/gX3zrG8dmbVVVOs+ZcwK3q0Y2dizdB0mJ16oxgs2TiNhqaA2hqDSIQtsfOajKCReXn4rXDI2krNR+nQJTApVAgopcBjAKwC8B7AG4DYC/Uyz9WBlAJ4CBjbCoRnQvgbwCKAWwBcCtjLEhEfgBLAIyBlpH1JsbY3sQuJ3nSEVjErS8bVqIGcreriZagYitIk+eVuNGjmaahOYCHXvkEr9ccNbbdNn4wFsy4OOFzPfzKJ1iyyZ5yI5uRvfqg/dx7u/HH9Xvg9UgIKypUhqiEgNqzZdzBH6CYk4riQj8mDT+nPS4no7hVjzq5/VqDzRJZoZh/O54An//qNsMeZG1HvteDZ2ZfgqJ8r4hT4OBWffRzAB8zxpIZkX4A4FMAPSKfHwPwK8bY34jo9wBuB/Bs5N9GxlgZEX0jst9NSfxe0sTqoG5mRzxDmCRRlCHM7WrCaYBs74FzZdVBPLBiqy1oaMnG/bjtsqEJrRjqjp7mCgSfnP2qasWFfsy79qIop4ANdSdskwReudCQqqKipEeniVZ2IhH1aCxvM2t/d7NC4f12LLwyGc/R6Xl1pmeTTtwKhWoAdxHRpMjndwH8njEWinUQEZUCuB7ATwHcS1rypMkAbo7s8gKAR6EJhRmRvwFgBYCniYiYG0t4GuF10EQjVkuK8vDN57SFlD7T118et2qqXEiJoL+IvChSQNMZJyIUqg6c5G7/8XUX5UzwkPn5O00SnJ5LLkcrp4NE7G56/527otpmZA8qSpTLrpv7xfttvU44gdl+Q1GZcc5ZY0qjJiOzxpZ2umeTTtwKhWcBeAH8LvL51si2O+Ic92totgd95CgGcJIxpisE66FVdEPk3wMAwBgLE1FTZP8T5hMS0Z0A7gSAwYMzH0eXaHSmlpSuOkrtALS9PKMG9XQ92Gd7kIkV+QsAfQoTS3Ux2sG7Y0RJUcI5ctoL3iQh1nNJ1CU40yQTd+N0TKJ2N/0+vbh5P3791g5D5aMyYEPdCTDA9WSL99vmOuGbdzfgiTd3wCsTFJXhoevLUd/YisaWIJZtqY86blllPX5w9fCcek65hFuh8EXG2CjT5zVEVB3rACKaCuAYY2wLEV2ZZPtsMMYWQ8vDhLFjx2Z8FZHI7KgtKZ29WeZQ/EQG+2wOMrFUABIB489LLCleWb/uRq1nnSvKinHL8x90uJQDuTb480hX/iFzAsdEV6/FhX7cPG4wnl5bByWy4tSC96oBkOviRU6/ra9URw3qiRvGlBoBowtWb4tkFFYgWSKlJRBqD53qFDaeTOA6eI2IzmOM7QIAIhoGXsa3aCYAmE5E10Er59kDwG8A9CQiT2S1UApAd1Y/CGAQgHoi8gAogmZwziqJzI5izawVVcWGuhNG7eNsqxgStZEAmipMJk0gfP/q4Qn/5sqqg1hVfQjdvBKCioq7rizD/7y3O6EcOQJ3pDP/kPmYZFav9Y2ttpTvMkmApYZTPA+0eL+9vu4EHjCpq4zfs0zSzoQU3P7Ch3jU4roq0HArFOYCWEtEuyOfhwKIWaKTMfYjaIV5EFkp3M8Ym01EywHMhOaB9C0AKyOHrIp83hj5fk172xN4JDI7ijWzDqvaC3b6bBgLX9uW1Zlxslk9C3wyVnxUjz+u34Pfv7sLz7xT57r9vOjVZ9/dBZ+HnyMnl4RCLgjxREkmnYPbVXGiqyR+PIIK66tyNqzEdaQw/7a1frNm/3I3ZIQUlpbSsp0Rt0JhA4D/AXA1gJMA/glt8E6GeQD+RkT/DeBjAH+MbP8jgD8TUR2Az5FDld3czo7MAkQiwpmgPbXB/FdrEVSY8eLdv2Irygf0QK8CX8oDT7L+4/FmkPqLuHTTPvz+XW1eEFIUV8fqcAccWbKp2nKt6ExHzaiZTDqHTBUB4k2sHrq+HI+sis54qqoMjS1BV/1/6aZ9mP9qLbyyBIUx3HVlWUz7lxO662pHEfbtgVuhsATAKQALI59vBvBnADe6OZgx9g6AdyJ/7wZwKWefs27P117Eizrm0Rao1ITvLKmMmrmEFBYZCNuERTCs4qu/XgdJSq0oeSr+425m5w3NAcxfvc22XSZ3OXycolcfmVZuFGjJNTfOZARorqBF/ZItnUOsOJdMerxZJ1ZaTihCGKacUwy47qn1eCJOJbWlm/YZKbT1d+nptTth00e5QHddzfXn2Z64FQojGGPlps9ricg+QnQiUpkhaoFKfbFo5qjo2dFUbQC0ojBAURhCiuaUlejAk6r/uJvZoKYXJgQtgaQhl7nsnQac6aMHxk06ly3SnT20PSntlQ+SKEqf7iadQyY93swTq8aWoC1iH9AmSbH6v9PkxCNJ+O6XzsMz79TBK0kIhMNQGUVlVZXJZl4wXFcFbbgVCh8R0WWMsU0AQETjoEUpd0rSNUPkvWDd/R7cv2JrVN4WK3o6BbcRl8n4jyc6G+SlLQaAR6ZVuL4nTgNOrnryuEmLnYvCDEht1t8ez+NQE9/mAcQWvE6Tk6Ci4uZxg6MKI/GCD08Hwpj/6jZ4JCCsaK6rufbsso1boTAGwPtEpEeADAbwGRF9AoAxxkZmpHVZQC/4IRO/4EeiHcj6gk0o64ObvzgI/7txn+MxraEwvrOk0nWJymT9x61ZPt2miJYlQkjRVD+JGulyVQDwiDWwdgRbQ7bjXGLjrOqJ1XedJieqygzvvrieUgyYv3obfB4JC1/bhu55npx7dtnEbersmG8+Y8x5hMsg6UydDbSpjDwSodmS7z8daaudUkYAmm4zzyNrWRwtOXfc/HYqqY9ttZOvL8eg3vkAyJYOIJdnx5mClya7M6Y1zxROqeHH/ewtWF8Fv0cyEuY5sarqIOZy3iM3z0A8O42UU2dna9BvTxwLfvhkKMxe8CPZ8/MEQoFfxrOzx6Ao34um1iDuWvqxYV8A3K1Skp0Vcmsnm+rgygQsmNFWhawjzfTThfWaO7Ktob1xWlEVF/rxy1mjMXdFNWSSEFYV3D15eFRqbSemjx6Int18+O6ft0TVwXDzDOI9u6446bHiVn3U6eF1lgK/jPnTYhf8SOX8OorKjBl5Q3MgabdAtwO2uePHS2WhMAh/bguZct3sbMSzzaWi3qoo6QEVibszx3p2HUEl2B7Er6TeReC6TKrxC36kcn4A8MkUVb5R12PneSV093uQ501vicqVVQcx4bE1uOW5zZjw2BrUHGxyVTv5kZW1naK4eUNzANUHTqZ0LZl+RomSjmvKBPqEw4w+K9cpLvRj1KCeSdnqknkGTscBMATY6UAYZ0OaF1Su3dP2wJVNIVdJt00hEyUJnc4fVFRMLOuDdTuPwStJCKssqmJUJpaxTvpU3VWWVzzdzNPf/AKmjipJS1uyQTpmgtYo2myrGjQ7laaCUZiKRTNH5czs1qm/rY4ksUvHfUv2PbEexysN2t3vwV/uGNcpy3SmoxxnlyDT3hrm82/a3YCf/2M7AM3XH4hW02RCd++kTx1RUoQN8yajvrEVNYea8NArNeA4eOCHL30MlbGcGXQSIR1uxrmmXmhoDuA+o8ynJszvXVaVM8F1PO+tWWNKMfXp9Wm7h8m+J9bjhEqwDaE+spDscjaR85f2yscT//qM+/38V7dlbMkaq+Pr131NRX/c92V+sjs9f1NHXFK7UWXEwixUckW9UHvolM17J6wiUvYzN5g+eiA2zJuMv9wxDqvnTMSyLfU5dQ91ck0lmE3ESiELPPTKJ7Z6CzqZDLvXO/79y6tAkMAQHdCkz4TlGNkCUvWyyZZ3R6ozwdz0OHJS/eaWSliflVcfOJkz95DXD3M7rqP9EEKhnak7ejqq1rGVTIfdV+79HJrZQHsxK/d9bqTz5rnkWkllSZ1N9UuqeX1iCZVUBV2yx1eUFMErU9QEwysTKkqKEm5DOklXkZ5MEa9mRFcVBjpCKGQY6wvyz9rD3P08EsEjU0aXrLwayXqt5ZagAo9kXyJIBHgkwO/xpJQgLReSy6UyE3QSKuvrTqQk6FLNsfXkjaMwd4UWZa6oDItmZlfloVUebFuJPnHj6JSK9KSbXOiHuY4QChnE+sLPGluKFzfbC9cDwD1Tzsc3Lo0fuJMKTjWSqw6cxFUX9uVWjMv3ynj2lktQlO9LaUmdm+qXxLAKFQCGd00yA0w6BqhcUnk0NAfww79VRZRX2vX84G/Rhu9st7cz9MNMI4RChuC98Es28gUCAHy1HXK6Dy3u5ri9uNCPR6aV4yd/j85xrzCGipKilNuWC6qDdKivzOqFVHXk6RqgckXlsXFXg82awSLbza7M2WxvLvTDXEd4H8UglaAgnreLE3leKWZ8QLrwemSbEVkmbTugucL+9Gsj4JMJBT45rR4Y2fbuyIT3UDw7Q7y+k44BSv+duqOnsx7AdqL5bELbs0G2+2FHQKwUHEh1VhmrNKfT/pmmtFc+JImgmNREkiXH/uzLhuCaEZmpb5BN1UEm1Aap2hlS1bHrfZSpDAGFIc+rTUKyFT8xsewcAJ86bE8P6fBey7YKK9cRQoFDOnS9xYV+zBpbGlNl5PdIIEK7zlSsEey8iPZYy/tUX8psqQ54QvpMMIwTp1ObxaZqZ0hHIkMd/e9sGU7L+nXHbeMHR/X528YPRlm/7mk5fzq913JF5ZaLCKHAIR2zyobmAJZV1jt+n+ch/PfXRqQtt5Ib6htbke/1RIXy53s9rq9r6aZ9Wh56Wato5ealzJWsk/qsfO6KaqNEqsKA25dswRVlxfjzHZeldO5U7AzJDFCxEhlm03C6YMbFuO2yoag6cBKjB/VMm0DIda+hXOnn6aDLCwXew0yHrjde9lEGaleBACR/XQ3NATz19k6jMJBe9SreS9necQnxXkwGRKnOdN6ra0DlngaMPbc45TaU9spHayi6LNjZsGK7x6kOIrHUk9k2nJb16542YaCTy15DuZb+JFW6tFAwF9UJmiqJ8XS9D11fbqREcNMJS3vlI6jwX1qvTFH+5E4DRLpnH8nosFdWHcT9y6u5EdiyxI++rjt6GuvrTuDn/9iOQDh6Zlc+oEekFKO9gE8qxHsx9Zlm2CHYd93OE2kRCm/UHLGnnrDcu3R5Qc0aUxoVd+KVCbKU2ViXbJGrXkO5voJJhi4rFHg62Z/8vQZgmrHVrOutOdiEha9tswmPWBQX+jHnqjI8+eaOqO15HglP3DgKg3p3Q0NzIMooGVRUzLmqDNeO6I/Xa47gmbV1rktyumX66IEoH9DD1fK+oTmAB1ZsdUzJEVLs0dcPv/KJLUBOJ6youOY37xnlFD0S8MtZo23XlagwdPNixlu5TTq/T9zfcdOOR1+ttW1n0PIRTRp+TtoGkYbmAJZtiVZPEoDVcyamfZaeC+RC4BuPXF7BJEuXFQr1ja1QeYXoV9WiKN+LHvkeVJQUobRXPm5avNFReMTi5nGD8fTanYYOGwDCqor7llfDJ2tCQFFVhFUYnerJN3dECRK9Upt14Eh2FZHILLX2UFOMSrrAI9Oii57zIqbNaJfCoj7PXVEddV3JzKLrG1tt0djWFzOWuuWKsuK0rBL0dvCFKDP2SccgwjuP3yOn3bU5l3Tlueg1lKsrmFToskJh8+4GbgRvWGWY89ePAWjL8e9PPp+b/mH+q7W4ZoRzwJn+Mj08tQILX9sWWQlo9ZcDYZVbljMW5oEjWfWD0yy1fEAPW357PU9/wEHf8uNrL7StlpwipmMhU3QpRLftM1NzsMlWU9v6YlpnmmdDYVx9YT/cccW5aREIgDZAqBxvLnM+onQNIomeJ5nBPRd15bnmNZSrK5hU6JJCoaE5gMf+uT3ufiGF4ak1OwHOfNkrO8/urC/TQ1PLMaKkiFt/2S3moKhk1Q+82SVTGa57aj38JjXVhLI+kXrSHDsCAXdOGoYbxpTavhudRDGSQMQI29AcwNrtx2wCWG+fVyKEFDWqEBGgPcuFr22znfehqeW2+5HpmWZxoR+LZo7CvUaNAy2n1ZM3jjJ+K12DSCLnSWZw74y68ngkIzgbmgMYUlyQ1sJB2aZLCoXaQ01wsAFzuX3iUDz77u6obQrjZzPlvUwLV2/DhnmTuZ4pbvB72qIuU0mtwPv9gMIAMARNaqrFt44F46jW9PH6L5v240/v77UNLjw/9XhIEuGNmiNYsHobJAJaLVlajfZFPlvrRW/cdQLWyXmBT8YIh0yhmZ5pTh89EKfPhvHoqhrIkgTGSWOdLuHk5jzJDu6dUVcei2QEJ++YzlClrYumuYilKY8mqDD0KvA5pn+wpjOob2y1DahMZahvbOV6pjiR55Xg9xDu+/JwvP/gZKODpqp+IIp97V5JwqnWUGQwjkZlmm9/rDQRC2ZcjLfumYRHp5XDJ8fvXj6PhIdX1SAQVqMEQoHfnpJDRy9E9PArn2DOX6tsqjgngd0e6CuXkAqcDasIhBn3PhUXpqeYU7zz8NKtMKblI4oFr58FwgoKfHJK7c1FkkmBkotFl9JFl1wplBTlJbT/E//agY0PTralf+DNFMoH9LANqAGFIRRWMH+1Xc1hxScT5k+vgFeWuN5Bqagf6htbkeeRY6qvQqqKHvle5HmluLUVnGaOZf26oyWowO+RbG65eopn4/fCKnfVdtOYgXjBYcXhlQkbdzVwjdpeyV2EeKYMqNmaYceqYWBbHYZVzPnrx/hgbwMWzLiYez5zP9PTaEgSYerT641ZdKx7mEsG6ngk88w680qqSwqFlqBiG/R8MuG28UPxpw17YJ0k69XQ9BlZQ3MA63YcxwMrttr88BffOtZ27jyvhL0NZ1ytT1TG8Oir22K6oiarftBiJ6INsh4JkCUp6vcqSnq4Op/TCqWhOYCm1pBdIBBg1vV4ZcLtE8+1qeYA4Pn3nVVQwZCCP22wHwOAW1vaSiYNqLxBuDUUzujKJd71aKtD+43Ra2k4ubBOHz0QJUV5+OZzmwFEp9E4fTZsOFBYfzMXDdSxSGb13Rm9jnS6pFAo7ZUfNVsFNDVR3x5+m34a0IKP9Ietd3gJZFNbaMt0/qg0tLibK48jFjmvkyuqTjK68TdqjkTNyvU4AZ6AefyGkbjnpSqbgASAbl4JKvgzcvOAELReL0WvEiSCKxWTlZAKbNnfxP1OYcD9y6tRPqAHd7Dj6djnrtiKnt18aQumsw7C8VR2qRDPZlDf2ArZ0U1W8xhzEgpawRx74KIsEea/Wougwmy/CaDDGajNqyKZNIeGh663OyrEOiYYVvDvlw9Na7uytdrqojYF2IQCAPziH9u5oU3XX9wftYeaULmnAXOXV+NsSMWZkN0fPKCoKCnKN1LzFvhk+GTCQ9eXw+uR4bcoyT2S/QFY26VHDafK0k378JNXaozAMe3ckvGymldB1QdOonxAD8gOqb9bQ5pHFS/ozKxntd5h67V5JAnPvFOX8rVZCSoM1/32PayqOmj7jmfzCYRVfPfPWzDhsTXcYxJBV9GZyfPIMZ9hulO0y0RYu/0YGpoDKPDJMdWATh5j+rPkuW2HFAavRZjLkvabtYeabO3R1Sq5zPTRA3HPlOEIKiq8soSFr22L2xemjx6Ih64vx9mQgpAKPPvublz287dT7kOAJpAnPLYGtzy3OS39MhG6pFCoPdTEVTM4qR7+7+NDuO35DzHzfzZxXxIdRVEx9en1AICHri9HSGXwebQOVnOoCWRxt5RIcoivbaMloKDmIH9W7HYwaWgOYD4n0hZA1Mtq7ojX/fY9OE1wGYCHXqmx/W4iNSQAIKSo8CSwfyIEFb6Bt8Anc43oZ0JKxmss8HD78js9a97vtQQVPPpqLSY8tgZPr9np2NZZYwc6rhLqG1shO3SAmy8tjZpcAFo/fWRVLb6zpBJnw7FjRnKRpZv24ef/2I6QwtASjN0XzDUsFqyujVpNhxSGuSuS70Nm1XS2jNhdUn10qjVxt1A3KAxQQirmrqgGQAiGVSN53MLV2/DQ9eVYsLoWBIKiqrj50kFGkrlYLFi9zRYol4jeVo+0tQq0QFg1vEl4aggnVRigCdCNu05g6qi233RbQ8IvE0gi437EwyORbRByg3mGqi/DdQO4kypPJn4+J7ck4gjAu+f3L6+yqVp4z9qs8uOlaNeD+V6pPuzYVkVlaGgOoLEliKoDJzG0uBu8HhmlvfK1Z+ngt7108wEwxuCVCT6PhJbIb+nR1FpNb8lwKsj1YC6nSRMvt5ce1CmThJCqgDiWQqecYPGIp5puLyN2lxQKlXtju+OlDCNInLQLDS1BqAzGy/bnTfvANwFGY+1kWk4iLdrYjd62tFc+d1D1yYSWoGIEjllnhl6JEIoxGK/dfixKKADA5Av64vWaI8ZniewrMEaE1yI5errnebi2C79Hwo+uvRCDeuXje0s/cmxDLEKqipqDTbhp8UZjQJ0+ckBM205LUEHNoaaU/M3jOQLouuKm1qAtWC+oAA+/UoNnbhlj7GsVHPcuqzKcA/RUKcnw8keH8PJHh6K2eSQtqd6imSPxyLQKLS7Egm5j8HuAuV+5AIv++VlUeg2ZCCpjkJmb3p1ektHD1ze2witLNicMa26vhuYA7jMCE/V97denqIm7RPNysUW1pR1XW11OKDQ0B/DiBwfSci6vpBk9rQQUta3PRAgqCp5ZWxdltIuhiYrC2smWbt5vizaONZMoLvRzX3BJImPQ9Ehky5sjSYCP7CsMnVeqDuHHEYOcU1oMibTob/NA7Jfbyo9OHz0QPbt58R9//gitJjuNRyKcOhvGd5d+FHXPvDLhrivPQ3NAwZKN+xwz0fo9hIemlmPh6m1RA+qyLfF1swtXb8M1KdbMbmwJYufR0yjwyY6z/jPBMLcPvFZzBPccPY2yft25ro9hVcuhlWiqFDeEVYawynDf8mps+tHVqD95Bs++w/f08skyhp1TAMXinaGr50KRQba9DM3Jej2V9sq3XQNgz+1Ve+gUN87IPPGxZkB2Cy9/FwB088lQWfuutrqcTaG+sRU+T3ou+yvl/ZHnlYwyiDx8MiHPK2HOVedD5jx0K7xz3fTF0qhVwjNr7cbZoGLP2W9m9mVD8NOvj4DPI6HArwXg3TtlOOZHBk1z7iA9QG/RzFF44sZRjh5CSiQIqs09l5dLCrbByzrrqSgpskX+tgQV/OatHTbPF0VhePbd3XjpwwNQmQqvTIYBXwv4kyIBf1djRElRQjYOnVQNow+/8gmm/God7l+xFVN+tQ7/ubQSDc0BmyE+1qTg+Q17ACRe1jVdhBSG2kNNuGPiMPgd3peQqqKipCiq5rFPJtv+mTQ01x09jRWVB1C5pyGhYDKzjUZX+ZmdQ376tRGcTMj8ByYRkO+R4JWBR6dVJOV+y8vfBQDfunwINsyb3K4uvV1upVDaKz9tM6x/fXoUf71jHLweGR/sacBPX7fnU1IZ8OK3L8WZkGp30eTAmCZIzLPzZZX1+MHVww0XQ579b85V58edScweNwTXVPQ30oHrboVmZNKyu940dhDK+nXH0k37oDLndt+zrApeyVlHb8UnS7a8RMWFfkwfOcA2i+cNmiqiEwr6PcAfbhuLkiLNXlDga8sUmuyAyiuK4xZeptjXPzmKtz89jjlXlcVM323m5Y8O4r6vXGCzUQQVBSGFuYrHSJXdx1tQUVKERTO13we0WAXdJvT4DSMBICr3z+bdDfjZP6Lfg0ypPqxp2q0R8E6rZ6cVRbzYn4qSIq46VF+5AVqizHHn9kavAp9rNVZDcwALHAJb/7RhL+6YOCzm8ekmY0KBiAYBWAKgHzQRu5gx9hsi6g3gJQBDAewFMIsx1kiaM/dvAFwH4AyAf2OMJadMjgOv+lYyhBSGm5/bjCduHAWvQ04GBoZvPrcJfo/sylg65cK+WLfzBIKmqGNz53ZyMbx2RP+459b1rQU+GQtf28ZVCykM+MN7e/CnDXvw/y4pjVlSFNDuQUhxn65ZJk09093vMWY/Dc0BvPxxci53PllGUb4PZf26c1/2x28YGZWgzo2WW1EYNu5qwPjzihNesq+vO8HdHgir+O3bOyC5XLn45LbZdc9uPjwxUxuAK/c2unJOSAdP/Osz/OKN7Xj8hpHYMG+y0Xf0xG/r605gwmNrohI//vKtHbbz8JIT6iTri88TvtbuzBNG8eI64rVBlghqjPEjqDB89dfrIEmkZQ9wocbS1MEOBbmyECWdyZVCGMB9jLGPiKg7gC1E9CaAfwPwNmPsF0T0IIAHAcwDcC2A8yP/jQPwbOTftLJ0834X8zT3BBWGe5ZVYXHEMGhFNy+4HTjf2n4M1mHL3Llbggr8MkW5VfrlaHsA70Uze01oBrXYqqywipgCgQD4PGRTGXXzyQgrKsIK497n1rAeMFaNnt28qCgpcp2gUCbNDmJWKcXKHjt3xVa8dvdESKZgMjfTARXAnL9+DK+sZThNZOneJ8bLG1IBKcaqy0wgrGDT7gYs+uf2uPmy/B4JjDH4PbKjnQJwtoE5oaszHnh5q5HQUe9XgD1Ibf6r2+C1qEhjJSdMJfLZKU275vkkOwagpZKewk2aGCDihagwY79YNhVNHezsNpwNd96MCQXG2GEAhyN/nyaiTwEMBDADwJWR3V4A8A40oTADwBLGGAOwiYh6EtGAyHnSgpM+PlUUFbjzz1u4S8tE8ckS7pw0DM+8U2eoC+66ssz4vrRXvhbvYHrzSSJbxLXVfdGN10QiMMAmEDwS8PtbLgFAuGvpRzgdcH55AmGG7/7lI6iM4RtjB8X8LZ9M+PF1F2HaqBJsqDuB+5dXgSCBoc3ds/rASW5Q2uw/2mNLZHJn5A8pDPcvT8xIOv68Ylt+JzPx+odX1oQeEfDzf8RP7w5oz+L171+BlqCCA5+3YM5fq2z7yHDv2GA7lghPvb0TSz/YD58sQWEMd11pV4V5ZULIIsHCMVKhpBL5/HlLkLv9rivL8Oy63UZ8UPc8T5SgSSU9hRbjkviUMpbQqW9shU+WEQhHvyseieCRs1NatV0MzUQ0FMAXAGwG0M800B+Bpl4CNIFhdguqj2yznutOIqokosrjx48n1A495D8TKCw5gWBtzdmwgpvHDcaGeZPxnUnDABAWr9ttBDaZjWLd/R7keSWjfnTd0dNcY9vGXSdcZ2dNlYqSIlSU9HClyz8TCRJa+kFsdUhQYZgYSaHwj5ojCCqah1dQAf68cS9WVx9EKKxwg9KOnrIPHokMjkFFxUMrP3G9f3GhHwtmVGj1khPsah7T/k7FjXg8Mq0cZf26Y9SgnuiR7+XuoyD5CUtLUMH/btwXFdj11JodNs+vkMJw/1cvgNnOrDJgA0elxgt0lIhQe4gfqGmmoTnAVVNNOr8Yv1+3G8Gw5jjBMzbz3h83A+/KqoOY+vR6UMRLKZFHG0voaOpg++RJURlWz5mYlZxRGTc0E1EhgJcB/JAxdsqcB4Yxxogooa7KGFsMYDEAjB07NqFjeTmPso21NeYi7797p46bB4lXP1oz9irc+IgTzfxZVbrJ93qMxIHWovKxiKdZ88qElz+qx+J1u20D+of7TuLDfVUguA9yc7tS0Hn9k6Ooi7iIxmNl1UEsXB1JaAgVc750Hp6NDFQ6TivKMENS0/lxQ3sbf1eUFHHtJulYxZoJKgBZFITEGJ745/ao39EifNtUhbHKo54JKvjOkkosmhlbZedUb3vz7kbbTN4rSag9dApF+V5DnTqhrA+emDkKJ5rPYmLZOXHrlNceOmUkv9SRIjeZ13V1NafZphCr+BFv8cEA/GXTPnz9ktJ2z32UUaFARF5oAmEpY+z/IpuP6mohIhoA4Fhk+0EAZj1CaWRb2tAqY43ED1+qahfvjWRg0CKFz4bUmHWH9U6i1482XhDLoBJSVXy0v7E9mh6l37cWlY9FvDUFY+BmUo3aB3Ad9ZyMGiVW4jgdXgDS/7y3G49M0+IldJXel4b3wT9rj8U4U2Jc+5t1eHLWaEwfPRCNLUGuYlBliQvDeFhPxVupAW2qQkVVMeeq83HzuMHGjH2uZbDV60/EUiPxsv1qv2/vSa2hML6zpNLIAjxrTCle/GC/sXL2ytsd7UaxIoxj3ceX7rwM555TGGXX04ULwIzSrLGC1QDgfzfuw4qP6hGORIW316ohk95HBOCPAD5ljP3S9NUqAN8C8IvIvytN2+cQ0d+gGZib0mlP0Jk+eiCOnjrLdR/NFXQ3zzOh2LpP3ozJ+uJ/pbwvVsVIdZAufCb9J686XLJ4ZUqbt1gqDC3uFncfJyPmiJIiw3tn0+4G17YCAPjel4Zh/Hl9cLDxDH70d3t0MaAZj/WB1MkASwC+OqIfXv/kqOvfTidnIo4QT765A0+vrcOimSMjgYs+/MeSSsMBAYhv+F1fdyLupK6bT4aiqraa6NbVq56ryCqE4kUYO+GXCV5LQsSlm/bh4ZU1xnup1353846Yjf3tlWk2kyuFCQBuBfAJEVVFtv0YmjBYRkS3A9gHYFbku9ehuaPWQXNJ/fdMNeyC/u7qBWSLkAJu3plZY6OD2A58fgYBS/Ix6/j5+idH0B7855XnxawOlygE4KlvfgE98r24c0klzqbBKGJVq/hkzSAcT+Z4JNhedB686zYHFTa1hvDYG+4Fgt9DuOOKYcYzf/vTYxHvNDv6QOokvBiAf2RJIFgJhFVjkDvw+ZkogQDE1sHrg7VTKnBAu2+awwNc1USXCDYh5KSiyvdKtpKxZsiUJcApal2v/Z5ISnUJ9jZmiowZmhlj6xljxBgbyRgbHfnvdcZYA2PsasbY+YyxKYyxzyP7M8bYXYyx8xhjFzPGKjPVtkQrr7Un3hj9ZFllPRqaA1hZdRDjfvYW5vz1Y8OzJs8rwScTrMGniVlskuc3b9ehco+WU0pXDfgStbSaYADe33UCJUV5UHlFLpI8p5mgyyAwIrjyTtGv22po/c3bOzDhsTW4c0ll3N+TiQwD6KKZo6IGgcdmjnQ0XgcVBW/UHMbNkYI4PJK9i7z0C6miqgy1h05h4Wv2oK1YtQx4kdEeieCVEXXfJg3vi4qSIleTk9aQihqTgdupSJTfQ5h3zQUo8PGHTb9Hc/pY+Nq2uFHrHlnCNy+N7XVn5kxIxdLN7ROf0uXSXACIStiWa8RyOtGMZk2Yu7zK5k0UUlQ8fsPF9u0MmDFqQPobaoEBmPk/m/BwxFOHgV+zIhFe/OAAvvrrdTFnhani5syXDO7leoY2oaxPVB2KkMKwZON+ze7jKqKd4ZnZX+CmNtA8m0bAKwHeyMLFJxMk0n7n2Xd3x0ztnixubTVfGMSPR+ARVBhOtYZsSRgL/DJGDHQ+Dy94M6wyqCpwy2WDo+6bLqT9nvhCbUGk7reeyvyupZoNxCtHC+lpowZyB/rvXTkM7z84GYN650fiYmITUlRXKkkzyyrr8fa2zI9dXU4oZCpWIV3Eev2Cior3dzUgyHF5UFTg/pc/sUVW53kl3DBmEJ7+5hcw85KS9DaWw5KN+1G5pwH3OlRtSxSFtXeeTTsf7mm05dBxqm+g+Z0n/1ppwx05eqssWF0LjywBIMwYNUALEkzSHTqd+GRCoS8xbfQrH9fbkjDGyzCqB29aUSLOCG/URg+a00cPxJM3juYeY0Yirc6K2aU7rGrbf/7/RmDxrWMMnT4vT9K8ay7C+roT+M6SSsN+EouwwhyD+mJx+5ItxsQrU3S53EeZjFXINN+eMBTPrd/j+H2YMwqHwipuf+FDeCRCIEGjWbK8UXs0rV4u2Uav0W2ODneKxNU8Y1K9z/abF522Wft+ZRIOBJpKj9LQxmiCCsN7u/gp6b2krVitvLXdHmcUrwwmL3jTzPxXozPcGtl743RI7XaQ3Y7AgHuXb4U/klpb957SHQfM3kXzXuYnheTBoKmErqnoizcS9ESLV1s7VbrcSiEXYxXcIBO0oLA4ndsq7hSmqRZaQ2pa03vEwsVqvUPBwNDUGuRmOuUFSN1wSfKugx4JhsuiTt3R03j2nV1pCUC8dkR/PHHjSPji283TBk8g8IinOgLMKiH+0OWVtQA4vTKa24H6/q8M5wZdBhSGYFh71oEww5Nv7sDlv1iDDXUnjBK2QOJVBwFgzadHsIYjGHUG9nQWjk5eZumgy60U9FiF+zgFyXMZzVMh/mib7Su6oF8Bfv+e82oml5k1thTNZ8M2mxMDaV4sqoq7riyzpdNgKkN9YyvW153AvEgh90Qwe0URETbUnTBWHtZMoKmysvowPjt8CmoWCuDEw21xGj1487n3dtviV4JhNRKXIHODOXl4JWDcsGJT7EQ1JBCCqlYu1hqjYPaeihWMF4//3Rj7uR475Vx+06m2djrocisFQOtUm350NZ7+5hfQUTRJYZXhlXYs3p0oJUV+/PG2MfjsaEvK50rlkaQyA15ZdRD/rLUb8kIKM1YFT6+ts6kiAgpDKKwYKwirnjwe5rOFTLWleZlA08H2Yy1JlTfNNPFUR2aKC/2Yd+1FUTVC9MSAgbD2vIIKcxVnEFJhePYwaGrY1rAKhVMLRMdaI8KaPsMjaauWAp8MrwRMufCchAdbr4NtigD0KvAleDb3dLmVgk5xoR898j1ZN9Alwlufpi8KNt0cagpwc9wkAgG498vDcfO4wfj569uwwlIq0g0MBJnixx7wUJT4x8kSQZYQlZrAKxP2NpxJW8CeXn5159HT3O89Etotl1U6yPNICKsqCLGztA7qnZg3DhBdI6SpNYTvvPAhwiYxKxMgyxI8RDgTchbWyyrrMWtMKR5YUe0uWSInlsJak+GNmiOYv3ob/F4Z79WdAEmIH75vIqwyI0GiGb+H8ML7ezF9VElG7ApdcqXQRgdZJnQQ9n5+JqXjGYDxw3prKYq9yc1X5n7lAvxwynB4SMsMmghubISKymy5akIKw9DibmmrkNYaKRYUdjAG3zlpWEoxIO1NWFXh90gu0nYnN0PT64ys3X7UtopTGPDi7Zfi97eOcbRD6KzbeQIy2ffxyYQbLimB3xM/iV5xod+o773wtW1Gcr5A2N5vYuH3SHhkWgVXk3E2zPDbNXWY8qt1GfFE6rIrBUAz3KY7H0xXZkgSMz0rNy3eDFlCUv72EgFPvvkZwi5m/MkgE3D1hX1tNgc9tcG9U4bbqo4lg8qAr/56Hfxeu1gjAH27t28q5VQJq0A4GHtE9MpkM7C7JZbdJc8rweuRMencYiyaORLf/1uV43kIgMKpdxFUGF6tPoxHplVgxMAiVwnqnCKi3fLkjSMxddRAdM/zRBWJspIJT6QuvVIoLvRjwddGZLsZnYZ4VdrcoDCWdACWlucmMwIB0NQ6b31qTxWhMM1A2juNel6FgevvzgA8/sZnnWoi44sUM0omhUM8uwtjbcbr8gGx09s8++5uPDytwpYVANAEw6Ov1rrOWMozPCe2uNN2nj56IDb/eAqWfPtS3DKOHwHtVO0vWbq0UACAESVFKGhP/7xOTKIG1o6GR5a4MS56Fo5MeoSYORNS0+JWfc2IvmloTWp4CHjuW19MOgNoPNdMc+3y372zK+a+YUXFocYzuGfK+fj25UNs34cU5qreA8Cv2/DDKcNR6Hc31ty3vBqrIo4lxYV+TBp+DoYWFzi2O510afUREIlbSFNunWyQqmNhrCphHQWvBEiSBEVV02KAlaDdU+td0ewJ9h/I88pGHYnbxg/GkjiuhrnCm7XH0M0rxzTAZhqPR0JFSfIJKmMJYp9MuHncYABa8N+q6tjeeyqAZ96JnaL9ICf3khNWwzMAPPNOdDYFJ/U1z+119Va+44WTh1SydOmVgl7L+N4pw7PdFINrK/rF38nE8v+4DEu+fSkenXZR3FB+Kz6PhAUz+MasjsTscUPw/oOT8ctZo5HnlVLu1Cr4gnbG6BIwxkux0KaiWDDjYrx1zyTM/cpwW8qRREklXYYbFMavQZBu8r1OCeRSLzdZ1q87bhs/2LZdIuAJk0qqvrEVfheZbuPhVAbUCd3wrNdASWT1YHZ7rTt6GlX1p7j7xTOgJ0qXXSmYUxUEFBUecud9kkkIwD9q3ac3njW2FF6PjIpzClFR0gO/eOOzhKzmj0wrx+xxQ1CU58Wcv36cRItzg6Uf7MfdV5+P6aMHonxAD1QdOIkdR0/hT+/vAyG+0dpNQXu/h/D3jw9yV5VW//raw6fw1No6qCmuwGaNLcXx5rNpLchjox1WibwCPxIBS28fh7HnFqd8/gUzLsZtlw3FP2uPoCUYRvmAHhh/XnTtgXRpBIY4qHDcYl09NLYE8ZRDLjaz22ssNZknzZOHLikUeEXDM82EYcWo3N8Yc6nnpst6ZcKNY0oxtLgAv3xrB/7xyREj/87jN4zEAxFBx8vjruORgPnTR2D2OE1vemH/zORQaS9CCsNz7+1Gvs+DZ9bWGSqxr3+hBK98fAgFPgnBsKZa4t0SN5MBlWkpmq0+43leKSo1Q7LFWXj8ZbOmhhpR0h01h/gxC6mSacWR3yNhzlVlWLxuN04H2uoaqAz4xh82af3wMrv+PlHK+nWP6YGjz9IfeHkrJMBWwMotTWdDSbbQzhs1R7DwtW1G3WddcOZFVlbmVVQsNdmIFNRvPLqkUOBWyOIEiaQDmbRZUVV9bIHgxjXWJxNe//4V6FXgw4TH1kQJtQde3ooN8yZHJep6o/YI/uvvNVEDoUciyBKhe17bo28JKsjzSmkZyNJNvldGWFHizuR55TqXVWo6ZH2loNsKrMhEkGXAI0lcY7lMwB1XnIs/ctJ3mL1bgNRdEXlkSiBkmm5eGb+/dQwqSnrYdOmA5qr6k1dqAIIxQUkWXRUcyztIn6Wv3X4Mj75aa1Q1S4SFq6MT7sVrk2aYJlSU9EBxod/QUMhE9gyxTHvH77qyzChZqlPWrzuuu9heOc9tAahE6JI2BZ67mETISPZUhWmqiRaTnzZPBxhPIHhlTUda1q87N/mWuYShrsOcPW4IKv9rCn7+9RFGoZSwygwjlp7EzSnfjNPduO7ifvB7JHSLpA6+7uJ+XDc+K4nqPiUAC2dU4IdpsvkU+D344pCetu35Xhl/uG0s5k+vsOl3vRJBkgh/2bgfDIiyv3gk2ArhpKPqHI/Lz+sddx+fTCnbMZyYenH/hI9RwYzB8PEbRsLn8PznR2oZJIteA+GW5zZjwmNrDK8dHsWFflx1Yd+k03xY01vEatNlP38btz3/IW57/gOM+9lbWLp5X9xUKEGFcQUoACyccbHtHQqriCoQlA66pFDgGXwWzRyFBTMqUj63m5cyrKiO1Zu8DoLpV5HC7AB/4NH1j9Y8/8WFfpSXFCHfax3s2jq3+X7oxmqfLMHnkWy+1X6PhIUzLsb7D07Gd790HogI7+1oAGOImQjue18ahkUzRyZUxcsjA1dd2BdDEixG4kRrKIyf/z97hs2QqqKipIg7WIRUZuQ+CikMXlnC098cjSXfvhSbfzyFWwjn8RtGpj1T7ILpIwwDttO5ieCqwEuiyASMLO2Z8HEPTW2ztUwfPRCv3z2RO3nQU5Mng5ustVYSKb5jJVapUHObHlgRXTI0rAKPrqp1lSzRSfAUF/rx8NRy2/aFq1MTqla6pFAAtE66Yd5k/OWOcUa1ptnjhuCWcXZPBrd080n4/uTzDWHj5wyqAGKH/JN9hu6RgPHn9TE+84Ta4zeMxPq6E9wZkxshMqGsD1bPmQgW6bRBRSt2LkkEv8dcfWqkye+7DoFwW9lBJ0OeRwLuuGIYJpT1ScjT6ZFpI1Bc6Mf48xI7zgm9Ju6cq8q4KQus99XnkWweXT5ZwqDeBZg0/JyYaorNP5mCwS4yfrrh+hH90avAZxiw5YhhUW+aXyZjYpOo4LUiQUsqaB68FQb88q0dmDU2sViCorxo7XRZv+6YP90eLOo2OyqPWKvmWEwfPRDvP3g17vvy8Ki+MGKAs11CV+24aRPvEcgScWuvWzkbVhzvx4iBRZzVrLvVi1u6pE1BRx8IdFZWHcRfP0jex1xRGW4eNxg3jxscpdf/yd9rovZToeVvf+wf27lFvXVvGC2/DcPdk+3qE54PNM/OYK4W9YClMIye6lnfdteVZVoxEZPtI6QwfO9LwyJCiaGkKB/VB06iqTXoSncuE/DLWaNRXOhH9YGT8HtkBOMUUgfacuvruuL/mDSMazdwwiNpKxdzLhxZIlz32/fglSWoqopbJgzFHVcMi+oD5vta4JMx9en1Ubo9NzNFQOtb6+ZNxtvbjuD+5VVobE1Mf/1v44cgqDCs2LIf63aewOW/WGOLw1AY8IPJZZh8Ub8oXfquY834zZrEqwveOm4wZo4dhKbWIO5a+nGUYdgrSZg9bihmjRmEb/xhU1Q7nOxx9y7bCpUhajU1+7IhAGkqI6+sOQSk4pYaa8ITj+JCP+6++nzjfS3wyVhfdxw1r37K3V9RgcXrduOZd+qiCitZqTnYhFbOrI8xhkemVWDha9uMd+7eKcPx2BvR4wCL4SVV2ivfdq/dXq9burRQMKMvQ1OxNZujJ/V/Z48bAjBg/qu18MoSFMYwa0wpnvzXDsff0vuTliVRcuyIZh9s3iBttjO4ESJPr93J9cNfvG43nt+wF0TA2ZCqzZ7JnTfj7ROHxlR7OaGoDJt3N+DGN3fAJ2szLLfZQX0y4cU7xuGW5z+IGtB1Q3pQ0QboZ9/djdLe3WxGTvNkgSdMExnAri7vj6sv6o8VHyWW9vzFD/aDCAgqiClEf/dOHW67fKjRpobmAH73buzIXSf+vHk/LizpgWsq+jsOtPWNrcj3eqIEhs8jQVUVW38OKvYALCA6s6nbtBFOOE14EjlncaHfmCDFWmUpjBnXzbsuQLv/C1/bZjuWoNmfpo8eiGtGtF17fWMruvmi72e+1xNV6c/M+roTUQGUXjn1WA8rXVoomD0WUvUa8XskI3rSyuzLhhgdQZ99uolC1HL5qMa+1o5ojrUIKoptkLbOIMyDXfWBk7brJaKoVYKOwgDFtF2ffbtRUix+by9Otobw+MzRthc4qKjcKGS/R8JXy/sZyeWCkfdFz1Fvnin5ZdKCzRhDvtdjDApjzy2O+q2AogKqCmteNmv5RitWYRrv5eN5wVw7InGh4JGliKtU7BVGSAVe3KzFaQDA0s37U/Ki0+9HrIHWKjDCCsOdk4bhD+/ttj1LPQ249b5ZV+mJYr7P5viU0YN6JpwcLhk3YvOEy0x9YytXsHhkYEKZpgK2XrvblY7eTvM9lqjtvOmiywoFa53dh6aWJ+Q1opW6bUsREVZUvFFzxNHnWu8IvMHY9W+aXjBerIVH0gZUnxx7xtTQHEBTa9CYMesk6pLqduhZVnkQd15xHsr6dbcNshvqTpiEhFYD1++RuNlG/V4Zz84eE/llQklRHlqCbfpX62BsVQVd99v3bK3WjZyNLUHHQcXtAMar3cwAPLCi2uWdakNRGRgnYyePp9fWGROSZ9buTPi3zOj3w0kYWgX72bACRVXxl037IUsSGFOjVgwtAQU1B5uMdNLpwHqfZ40txbLKem7NbDckMyF0Gri1Gt32N8Mny47C0e1Kh9dOp/OmQpcUCrwBdeHqbXjo+nIsWL3N1Sx+8a1j8J8vfmTkSFeYO59rJxWK30O4+qK+Nj9kM+YXrL6x1VYWUibCH24bi6J8r+Os1vxCaQFZ2nI1oKggxrgFzj0SuXLh80mwzcR1qg6cNAZb8yCrDz61h05Bt1log7edkNLm4sjDKb+9vv2RaRXaMzKhqAxLN+3Fsi1tM/nbxg/GghkXu/J91+H1qfuXV4FIilkjeMzgImzZ3+ZSKBPg9WjG70+PnMKzcXLxAJrxWzc0amUo49tsnAiFmZEg0kkYtj2zJnxnSSUCCgz1h1cmKJY+tPC1bbhmhDvf/njw7rOea4pnS3ODG7WmTIAkEfI8csyBu7jQj0emldvsiApzNqa7XY2mYj9JhC4pFLjBa5KEQb3zMXZIL2zY1RDzeL9MaDwT0uq3Wpb38dQR5pmBLBFCYRW3TzwXd1wxDADw9qfHYwol/QUr8MncspAlRXmOy2feC+X3SLjvK8MxoqQHbn5uE/c4J4FgTaYnyRLunDAYi9/ba9s3VkSm2eAdUNS2tKMWHpnmvlwjD56Rk1cDYcnG/eiV78Oz7+4y7EDxZp+8PhVUAK8cW5jWHj6NH0wuwzPv1MEnE8KM8NDUckwfPRA9d/hcCQXzwJBqjARjDFOfXo/HbxgZc6AqLvSjKN9nE0IeSQJBjZotO6laksHNrD7R37PO1s+ElKh+fd3F/bBwxsXG78ebJPDsiPH0/m5Wo8WFfswaUxqVKnzW2NK0rhKALioUeBL3bFjBHS98CDfZn0kijB7Uk+tepi+/Yz2o6aMH4vTZsNFp/vT+Xlw0oAemjx6IRTMjAoMT8Qi0qZAA2KKQ87z8iFwd3gsVCKt4/I3PoILhi0OKsWF3bIEYBWNR6qqHri9HQ0sQksUIfdv4wQkJKh4/vu7ClKNeAbuR89VqfuZJ3XtHV7HFm31qagP7vY+n35clwrPv7tIK0ahablY9araipEdM43qBX47y3mloDuCuK8vw1Jodrvoxj5DKEFIZ7l1WBVmKVkVahaJ2zdGNa+VkXE3nbNbNrD6Z3+PlJOKpE90OwGY7YqrGdJ2G5gCWbYmuWbKssh4/uHp4WgVDl4xTsPqj6wW/471IBT7Z8Gsv69cdj0yzB7u58bnWPRSCCkNLUIkKuNHjJ+ZPr+AGuIUU7fxOvxHrt51eqDMhrQ0f7vs8ZrutKAxQVRV3ThoWUb3V4pdv7rAZvC/o55ybhednnueV4IsUPffJhJ9+bQTunHReQm2LhTnqu0+hu8I4MvEDrPQ4D0DzPrOS55XgkCQUAIyAODPmme4vZ42G36PVhTYza+xAvHjHZUaMjR7Vu3jdbjBQ3AyrHgkxYz/CKowYFKeAMKsnjBPpnM3yYnRuGz/YFrOTzO+Z+0VZv+6YOXZQShXNzOdLB8nGZCRKl1wpANEzg6bWEO5a+lGUW5iVPK+E+dMrcNWFfdvcTS8bgpZAGIv+9Znm+x5jmWj1dOKhDwR6KP6D/2d/4cwqlERd8aKSghHZKnsRtECp1yzlJmMRUoFfv7UDEidZnM6jr9ZgUO98o9yiefbEm20CwOvfv8IwIqfLS0WfTZs/64Fx8UwmZ4L2gCKes4LVO0pRGX510xfwwMtbbffbJxMemVaOhaujXRjNM13ds+a6374HxWQkX1V9GPOuucjR6cDJDUAmwg+nnG8YpmsPNeFUawj3LquOmU3WPPjohnurJ4wTbmazidhveDr4H1w9PO2z8nSeLx0Im0I7oA/ADc2BuEvSsKJGCQRAGxR++dYO+GTNvXL2pYPRs5sXDc0B237mwePeKcNtnj5nQ6qtApwWgWvSzcqEayractAk6i5pPmbjrgZbuuyAwnDPl4dj5KCeeOyN7a6zKisMNuOimZACfPcvHyEYVkAUbaxjANfvOh01Z3leKi99WG/YQhbN1FQiv75pNO5fXg0igqqqYLAb1hmAxpZgVDyAdSBe8Oo2W7psxhgu7N8dqsVO4vNIeP3uiSjr1x3d/Z6Ywr0lqMArS1HqKfNqgqcW9MsERqQFIyoqrr+4PyZf2A/jzyuOOvek4X2xdNO+uIWWgorm5HDT4o1xbT9W4un4eV5b8byHrDr4VF1cU21Pe5COmAw3dGmhoGO92bFWDDo83+b/3bgP/7txH7yRmrPTRw/kDh5P/OszW8U0iaLLWdY3tmqDpylwKc9jdz9L5mUoLvRjUO9uXJvEoaazXBVQqrTNkplxTXNXbAXAMuJ3HctLRee+5dWYUNbHJlyf/NdnePGDA7Zzmj2oeAOxLBFkiq5klu/1oCWocF9m/Vzx/OxrDjbZbEXmGSJvBkkS4Xff/AKq65swqrQIrSEF+xpaALCoWgNLN+2zeWTxUBnwyKoahFUk7E4dazbLe06Jeg8li3k1AESvgLLRHjckMxFMFCEUIphdI+94odKm0vBbBuTaQ6cgOYRvhRSGuSu2Gg+PN3hYl+oqA06cPovqAycNm0E6lopOy2Dn87CMZIvlIUsEMII5QMsjSVi7/ZhtVZYobrxU9Jq7k4b3jRKu355wLlcomD2oeM9HUe1FPPVnNmpQT8eXOdbM1ClC1lzYhzeDHDukF25fsoV73RIBv75pNCaU9cH81fZz8+CpBv0eyZX7diybgpMnYLp9762Y73lrKGysYHXXbDOZaI/1vaw7etp18F06V0U8hFAwobnZeeHzkM3oHFLaBuSVVQfxwIqtsesjRLyEeIOHk4vnnX/egm6+tqhcw3WVtDQP5oHAjc4z1mDjtBStKClqt5rNvEG0Jajg0Vdr8ZNXPsGcq8635ZV3i9uUGqdaw4Yg1n+nV4HPtpKjyHYdp/sHwHhmQUXBv18+NOoY67XEmynzBs0Cv4xBvfOj2m2eQYbCCmb+D9+9GNAmIPcvr8Zz3xoLn0xGxHgy/Nv4IXjxAy1wjed5BMS2KbSXntyMkw0m5JBOJN3tsb6XY4f0wvq6Nq8/PU4mW3RJ76NYlPbK5w7aj0yrMOwPD6yojjtD0r2QeN4Sc79yAf8Yhihvj/IBPfDvlw9FSFHh80hY+No2rKo66Cp/vJuUwrxMscWFfsy5Kn4mSCfiZQ43f62oKm764iDkeaUoe0pzQEEgzPDkmztw+S9i58fX4aUMt3qYWZtG0FRIs5/bhPG/WIOlm/cB0Gavhf7o+VKh32NzEODdv+mjB+Kh68sRCKsIKVp+pXE/e8vxGuJ5lPAGzWBYxXeWVNqev+7tsrfhTNz7pdmr+EGJfo+WGdeaBt4rU1T21EBYNVZU/zZhiGMq6lgeMrz3IxN6cjO8e25G937LRHt476VZIACamrPuaPYKK4mVggVbcJnCjFrGgJZbJlaEKqC9POYU0zw9YPWBkzG9fJjKcO1v3zOW7bqRce6KrVBVFSGTbnfuiq3o2c0XFe3rdlnOm73ePG4wnl67M+o6ZdIGizMh52vX02y0OIU1I3r2HVa1WeTqORNRdeAktxqWXhAolk7XaUXES6kxd0U1ZJIQVhUwUCS3lHaen/y9BmDANSOcE8JZsd6/huYAFqzeFjXYhlVg7opq7jXEmylbVyR6vqiAAiNozHp/YgUK6qiqioqSHra+fv+Xh2PcsGJbChLdu2rBq7VR16arWZ97bw8enVaBBatrbe9HvJl2e+jJzbhZRabL+82K25QaZvtVeyOEgoWG5gB6dvPhkanlCIQVTCw7x3g4Dc0BPONQZBvQdLX3fXk4vlqh5b83L++tg8flZX1iCgVeugkA3BVKIKziu3/eAhVtkbfcGabinKfdTHGhHzeNHRQVOXnDmFKsqj6EWBmPvnPFMPzp/b1xz2/GGymBedWFffFfK/kGz1g63XjqF/N9j3ZDDuI/l36EkCXgbP6rtbhmRH/MGlsaZZi26sWd1Hf1ja1cm4xM/Gtw41ESz33aen/K+nU38gE58f2IOseaH8o8EE4o64PFt46BXk6yvrHVMY1GSGEY1Dsf7z94NV7cvB9Pr62Lm4PLeh/ay5Cr3/O5KzRhGAgpthQWmRqQ3ao13Qj2TCGEgomVVQdx70tVUQm9vPJ2w5NIeymcjWsqA559dxd+/fZOW9ZOs0tb3dHTeHiVfQA08hCFtU6aSII63ePFWkPh3mVVhnePyoANdSeijJi8gY0XObmq+hAeur4cC1/bBlkitFhm9H6PhDuuGIaLBvRo06mHFYBi174OhBUU+GTTi1ptm2m2hsKOwow385KIDAOyEyVF/MRlXllC7aEm24D60ocHDL14LFtNaa98rk1GYc6zZTcz5eJCPxpbgvh4/+c4YzECtIYUmzvzhLI+WF5ZzxXhXolw7Yg21+biQr+ttsasMaV4qfIAZJKgMBWLZo7ChLI+cQY0QnFhdI2CRGfa7RUfwPT/M4JHJjw8tQIjBhZl/HeLC/02G8KAHj4cPhU0PsfKANAeUKyCDrnO2LFjWWVlZVLHNjQHsHHXCZxoDmJiWR/0KvDhiz99i+uKSQQsnFaOCwf0wDf+sDnh+q5emfCrWaMx/rxirK87gbnL+YFC/3XdhfDIEsKKisf++VlSKZAL/DLmT9OC7ADg8l+siRJieV4JG+ZNxvq6E3ggokrRX3p9YFtdfQj3LY+2m/g9Em6fOBTlA3oA0Abd5zfsgU+WEVRUfHvCUIw/r9gWoBatslHx5Yv64c1Pj4AxQkhlyPNKYEyrhnbzuMFobAnimt+8F3WPvTLhj9/6IjcZXkNzwKgLYcYnA0/cODpKGOsOArJECCsqJg0/B299eizqOL+H8IfbvsgNZrzvy8Nx87jBtt/T76netlVVB3Hf8mrj+ckE/HDKcMNo/spHB/D3qkP4wqCeuHX8UFeD0MOvfBK1crPi95DxDOuOnsa1v10HB7svPJLWp++YOAw3XFKKtZ8dw89e3x4z661MwAc/mRJ5nnYnCwLws6+PwMBe3YznpA/wBT4Zh5rOAmCoKClKOV4hVcFRd/Q0rvvte1HvoPUZZko4Ve5p4DoB/Pf0chxrDmLS+X0w9tziuOdJtX1EtIUxNpb7XS4JBSK6BsBvAMgAnmOM/SLW/skKhZVVB3HPS1VRAmBSWTHWWQw+HZ3xw3rj4wMnowYwvwwU5XlxrCVk2/+uK4dhzafH8OnR5pR+97Jze+PcPt3w8f5G7DzaEqciQBsE4IL+hdh+xPn3S4r8mHBeHwztU4CwouJAYysOnTyDTXsauQL9ksE9cNmwPhjUqxt+/PcaV+m+Lx5QgE8Ot9i2SwRcU9EXb9Qci9IIE4AL+xdCAsPw/t1xTvd8VO79HEebWnEqEMbpgCnVMSeTrAeALAMBBRjY04+vjS5BWAH2fd6CfoV5+PxMEK9+4j7KPJMM7pmHfJ+Mz47Z748bJAK+P7kMV13YD3uOn8YbtUfRq5sXo0p74pFXo+0ReV4Jq+dMREtQMVRbm3c3YNG/PgOYFt/Su5sHxYV+VJT0QHlJEQ6fbMXx0wEU5nnQu8CLnt38kW1n0ad7Hhpbgnil+rC9XQC+eG5PHG46i2HFhVi384TxjMef2wuzLxuCfK+MtZ8dh08mnN+vuyEAgegofaco+pqDTXh4ZY1jcS0C4JEJc79yAQ43teLIqbP4ank/9C70Q08V/4d1dVi99TBaQgx5HgKIkgqu6xBCgYhkADsAfBlAPYAPAXyTMeboSJ2MUGhoDmD8z9+OGdIvEAiyT55XgqoyQ5UqE1KqjJgJJNL+01XF1toO+mePRDYninRhXeW4IZZQyCWbwqUA6hhjuwGAiP4GYAYAd9E1LqlvbHVdHEYgEGQPY4UbkQS5JhAAzU6nsrZ6EtbaDtYo+ozAWFqD63IpTmEgAHMYaX1kWxREdCcRVRJR5fHjxxP+ESdDoEAgyA26eWX4PJJWC1wQl2A4fmbmRMgloeAKxthixthYxtjYc845J+Hjiwv9+P7k5IOzBAJB5vDJEn5/6xi8fvdEUDulW+nozB43KK3G8FwSCgcBDDJ9Lo1sSzs//PIFGHdur6htV5QVoyhPdjhCIBBkGlkiPHHjSEwarsUG6ZHOeZGCFJ5cGq0iSKS1y6m2g/lzJnKK9e3uxcKvj0zrOXPJ0OyBZmi+Gpow+BDAzYyxWqdjUnFJBTT3sHU7T0S5gf3l/T1YunkfggrDmaCCkKLg1JkwQkzzDlAB9M6XMah3N+z5vAVgQMtZFQo0l6mibjKuvrA/KgYWYURJD+w81oylm/Zh1/HTkCRCgdeDIX264WDjWRw6FXBsW5FfRmtIgcoixiwAZyMq1kIf8OA15dh+5BT+/vFBeCSgfGAvjBxYhA11x7Gn4Qz69vBjxqiBKOtbCADoke9DSVEePtz7ORa/uwsHTraiu1/CmCHFyPfKKMyTUdqrACdbAli25QBaAloBdl7vIACFfgl5HhknW0LQ/ZiuKe+LgjwvfJFl/8f7G7H7eAuYCvi8QGsIyPcBeV4vBhTl4Z4pw1GU78UL7+9BS1DFxSXdsa/xLHr4Pdh2+BQONJ6Bqqo4E1JxXp8CnNPDj0ONrRgxsGeU95FXBlpDDIdOngFjDIoKNJ0N4dSZEIb1LcCYIb1RPqAHwoqKN2qPIqQoaD6roCjfi6J8L2oPN6GpNYQzAQW9C3wo71+I9bsa0NiqGQZlAOf26Yb+PfNQPqAIx0+fxdb6JjQHwiAC+vXIg1eW0BoIGd5H1QdO4vMzQYTCDPleQq9ufhTmeVDWrxBeAlZtPYwjTa04G9Y8whicvY8UAHuPn0b1oZPole/DpPPPwb9qj6KhJYQ8L+CVZYRVFc1BBinSR2MhASjKlxFWGBipMNfOKfBrJwiHgb5FfgTCKloCIYQVLTJ7YM88lPXvjmMnz6I5GMapsyF4iHAmrMAnEU61KghDM1bm+yUU+D2QoB1bXODF+PP64LqLB8DrkaO8jyaUnWNL6w0gyq1V90I61NSKt2uPYt2u41AUzdDq1vuopGc+Dp9sxfYjp3DyTAijBvXElIv64UDjGWysa8CnR0/hqxf1w9FTAby78zh65ntwedk5mFDWJ2XvI72i2z9rj+BMMISLBvTEhf27o3Lv53ij5giaAyHcctkQjBjYE0s373P0Pnrr02PonufBD6cMx9cuGYRk6BDeRwBARNcB+DW09/B5xthPY+2fqlAQCASCrkhH8T4CY+x1AK9nux0CgUDQVclBLZ1AIBAIsoUQCgKBQCAwEEJBIBAIBAZCKAgEAoHAIKe8jxKFiI4D2Gfa1AfAiSw1J9uIa++6dOXrF9eeHEMYY9zo3w4tFKwQUaWTm1VnR1x717x2oGtfv7j29F+7UB8JBAKBwEAIBYFAIBAYdDahsDjbDcgi4tq7Ll35+sW1p5lOZVMQCAQCQWp0tpWCQCAQCFJACAWBQCAQGHQooUBEzxPRMSKqMW1bRETbiWgrEf2diHqavvsREdUR0WdE9NWsNDpN8K7d9N19RMSIqE/kMxHRbyPXvpWILmn/FqcPp2snorsjz76WiB43be/Uz52IRhPRJiKqilQhvDSyvbM990FEtJaItkWe8Q8i23sT0ZtEtDPyb6/I9q5y/Zkd8xhjHeY/AJMAXAKgxrTtKwA8kb8fA/BY5O9yANUA/ADOBbALgJzta0jntUe2DwLwT2hBfH0i264D8A9opQ8uA7A52+3PwHO/CsBbAPyRz327ynMH8C8A15qe9Tud9LkPAHBJ5O/u0OqtlAN4HMCDke0Pmt75rnL9GR3zOtRKgTG2DsDnlm3/YoyFIx83QavYBgAzAPyNMRZgjO0BUAfg0nZrbJrhXXuEXwF4ANH1cGYAWMI0NgHoSUQD2qGZGcHh2r8H4BeMsUBkn2OR7V3huTMAPSJ/FwE4FPm7sz33w4yxjyJ/nwbwKbS67TMAvBDZ7QUAX4v83SWuP9NjXocSCi74NrSZAqB1ngOm7+oj2zoNRDQDwEHGWLXlq05/7QCGA7iCiDYT0btE9MXI9q5w7T8EsIiIDgB4AsCPIts77bUT0VAAXwCwGUA/xtjhyFdHAPSL/N1Vrt9M2se8TiMUiOgnAMIAlma7Le0BEXUD8GMAD2e7LVnCA6A3NDXBXADLiKirVHr/HoB7GGODANwD4I9Zbk9GIaJCAC8D+CFj7JT5O6bpTTq1X73T9WdqzOsUQoGI/g3AVACzI50E0Oo8mwuYlka2dRbOg6Y3rCaivdCu7yMi6o/Of+2ANgv6v4iq4ANopYn7oGtc+7cA/F/k7+VoUxF0umsnIi+0AXEpY0y/5qO6Wijyr6467CrXn9Exr8MLBSK6BppOfTpj7Izpq1UAvkFEfiI6F8D5AD7IRhszAWPsE8ZYX8bYUMbYUGiD5CWMsSPQrv22iDfGZQCaTMvtzsIr0IzNIKLhAHzQMkZ26uce4RCAL0X+ngxgZ+TvTvXcIyu/PwL4lDH2S9NXq6AJRkT+XWna3umvP+NjXrYt7Ala4/8K4DCAELRB8HZoxpQDAKoi//3etP9PoFngP0PEW6Oj/se7dsv3e9HmfUQAnolc+ycAxma7/Rl47j4AfwFQA+AjAJO7ynMHMBHAFmieJpsBjOmkz30iNNXQVtP7fR2AYgBvQxOGbwHo3cWuP6NjnkhzIRAIBAKDDq8+EggEAkH6EEJBIBAIBAZCKAgEAoHAQAgFgUAgEBgIoSAQCAQCAyEUBAIXENFeIupDRD2J6D9N20uIaEXk79FEdF0S536UiO5PZ3sFgmQRQkEgSIyeAAyhwBg7xBibGfk4GpofuUDQYRFCQSCwQESvENGWSA77Oy1f/wLAeZFaBouIaCgR1RCRD8ACADdFvrvJugKI7Dc08vdPiGgHEa0HcIFpn/OI6I3I779HRBdm/ooFgjY82W6AQJCDfJsx9jkR5QP4kIheNn33IIARjLHRgJG9EoyxIBE9DC2Kdk7ku0d5JyeiMQC+AW1l4YEWkb0l8vViAN9ljO0konEAfgctlYVA0C4IoSAQ2Pk+EX098vcgaDlk0skVAP7OInlriGhV5N9CAJcDWG5K+OpP828LBDERQkEgMEFEVwKYAmA8Y+wMEb0DIC/J04URraKNdx4JwEl9FSIQZANhUxAIoikC0BgRCBdCq9dg5jS00og8rN/thVZKE5F6wedGtq8D8DUiyiei7gCmAQDTcuXvIaIbI8cQEY1K/ZIEAvcIoSAQRPMGAA8RfQrNqLzJ/CVjrAHAhojReJHl2LUAynVDM7Q8+L2JqBbAHGg1dsG0EosvQcty+g8AH5rOMRvA7URUDaAWWolFgaDdEFlSBQKBQGAgVgoCgUAgMBBCQSAQCAQGQigIBAKBwEAIBYFAIBAYCKEgEAgEAgMhFAQCgUBgIISCQCAQCAz+Px4KsMQ1eIQbAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2025.1/samples/dbscan.html b/2025.1/samples/dbscan.html
new file mode 100644
index 0000000000..f2cee92c15
--- /dev/null
+++ b/2025.1/samples/dbscan.html
@@ -0,0 +1,701 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=132153fd" />
+      <link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/dbscan.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-DBSCAN-for-spoken-arabic-digit-dataset">
+<h1>Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-DBSCAN-for-spoken-arabic-digit-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">davies_bouldin_score</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;spoken-arabic-digit&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Split the data into train and test sets</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 6.37 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_opt</span> <span class="o">=</span> <span class="n">davies_bouldin_score</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn Davies-Bouldin score: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class DBSCAN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 469.21 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_original</span> <span class="o">=</span> <span class="n">davies_bouldin_score</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn Davies-Bouldin score: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn Davies-Bouldin score: 0.8542652084275848&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare Davies-Bouldin score of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Davies-Bouldin score of patched Scikit-learn: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Davies-Bouldin score of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">score_opt</span><span class="o">/</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="kmeans.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+<jinja2.runtime.BlockReference object at 0x7f04b7395280>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+   
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+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+</html>
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diff --git a/2025.1/samples/dbscan.ipynb b/2025.1/samples/dbscan.ipynb
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/2025.1/samples/dbscan.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/kmeans.html b/2025.1/samples/kmeans.html
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index 0000000000..c64bc6bca8
--- /dev/null
+++ b/2025.1/samples/kmeans.html
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+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
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+</ul>
+
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+        </div>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
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+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/kmeans.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Kmeans-for-spoken-arabic-digit-dataset">
+<h1>Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Kmeans-for-spoken-arabic-digit-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;spoken-arabic-digit&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((236930, 14), (26326, 14), (236930,), (26326,))
+</pre></div></div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_clusters&quot;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">123</span><span class="p">,</span>
+    <span class="s2">&quot;copy_x&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 7.36 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inertia_opt</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">inertia_</span>
+<span class="n">n_iter_opt</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_iter_</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn inertia: </span><span class="si">{</span><span class="n">inertia_opt</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn number of iterations: </span><span class="si">{</span><span class="n">n_iter_opt</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Intel® extension for Scikit-learn inertia: 13346.641333761074
+Intel® extension for Scikit-learn number of iterations: 274
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class KMeans</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 192.14 s&#39;
+</pre></div></div>
+</div>
+<p>Let’s take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inertia_original</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">inertia_</span>
+<span class="n">n_iter_original</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_iter_</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn inertia: </span><span class="si">{</span><span class="n">inertia_original</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn number of iterations: </span><span class="si">{</span><span class="n">n_iter_original</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Original Scikit-learn inertia: 13352.813785961785
+Original Scikit-learn number of iterations: 212
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare inertia and number of iterations of patched Scikit-learn and original&lt;/h3&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;strong&gt;Inertia:&lt;/strong&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn: </span><span class="si">{</span><span class="n">inertia_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Unpatched Scikit-learn: </span><span class="si">{</span><span class="n">inertia_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Ratio: </span><span class="si">{</span><span class="n">inertia_opt</span><span class="o">/</span><span class="n">inertia_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;strong&gt;Number of iterations:&lt;/strong&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn: </span><span class="si">{</span><span class="n">n_iter_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Unpatched Scikit-learn: </span><span class="si">{</span><span class="n">n_iter_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Ratio: </span><span class="si">{</span><span class="p">(</span><span class="n">n_iter_opt</span><span class="o">/</span><span class="n">n_iter_original</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> &lt;br&gt;&lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Number of iterations is bigger but algorithm is much faster and inertia is lower&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="ridge_regression.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="dbscan.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
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diff --git a/2025.1/samples/kmeans.ipynb b/2025.1/samples/kmeans.ipynb
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/2025.1/samples/kmeans.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/knn_mnist.html b/2025.1/samples/knn_mnist.html
new file mode 100644
index 0000000000..4b43e8e581
--- /dev/null
+++ b/2025.1/samples/knn_mnist.html
@@ -0,0 +1,714 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn KNN for MNIST dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="index" title="Index" href="../genindex.html" />
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+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn KNN for MNIST dataset</li>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-KNN-for-MNIST-dataset">
+<h1>Intel® Extension for Scikit-learn KNN for MNIST dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-KNN-for-MNIST-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;mnist_784&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">72</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((56000, 784), (14000, 784), (56000,), (14000,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_neighbors&quot;</span><span class="p">:</span> <span class="mi">40</span><span class="p">,</span> <span class="s2">&quot;weights&quot;</span><span class="p">:</span> <span class="s2">&quot;distance&quot;</span><span class="p">,</span> <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">knn</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">predicted</span> <span class="o">=</span> <span class="n">knn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">time_opt</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">time_opt</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 1.45 s&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for Intel® extension for Scikit-learn KNN:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for Intel® extension for Scikit-learn KNN:
+              precision    recall  f1-score   support
+
+           0       0.97      0.99      0.98      1365
+           1       0.93      0.99      0.96      1637
+           2       0.99      0.94      0.96      1401
+           3       0.96      0.95      0.96      1455
+           4       0.98      0.96      0.97      1380
+           5       0.95      0.95      0.95      1219
+           6       0.96      0.99      0.97      1317
+           7       0.94      0.95      0.95      1420
+           8       0.99      0.90      0.94      1379
+           9       0.92      0.94      0.93      1427
+
+    accuracy                           0.96     14000
+   macro avg       0.96      0.96      0.96     14000
+weighted avg       0.96      0.96      0.96     14000
+
+
+</pre></div></div>
+</div>
+<p><em>The first column of the classification report above is the class labels.</em></p>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class KNeighborsClassifier.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
+
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">knn</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">predicted</span> <span class="o">=</span> <span class="n">knn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">time_original</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">time_original</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 36.15 s&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for original Scikit-learn KNN:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for original Scikit-learn KNN:
+              precision    recall  f1-score   support
+
+           0       0.97      0.99      0.98      1365
+           1       0.93      0.99      0.96      1637
+           2       0.99      0.94      0.96      1401
+           3       0.96      0.95      0.96      1455
+           4       0.98      0.96      0.97      1380
+           5       0.95      0.95      0.95      1219
+           6       0.96      0.99      0.97      1317
+           7       0.94      0.95      0.95      1420
+           8       0.99      0.90      0.94      1379
+           9       0.92      0.94      0.93      1427
+
+    accuracy                           0.96     14000
+   macro avg       0.96      0.96      0.96     14000
+weighted avg       0.96      0.96      0.96     14000
+
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h2&gt;With scikit-learn-intelex patching you can:&lt;/h2&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">time_original</span><span class="o">/</span><span class="n">time_opt</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+      current_version = "2025.1"
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+    </script>
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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diff --git a/2025.1/samples/knn_mnist.ipynb b/2025.1/samples/knn_mnist.ipynb
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/2025.1/samples/knn_mnist.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/lasso_regression.html b/2025.1/samples/lasso_regression.html
new file mode 100644
index 0000000000..7cb8de10d3
--- /dev/null
+++ b/2025.1/samples/lasso_regression.html
@@ -0,0 +1,738 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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+      <link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
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+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
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+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
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+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" href="linear_regression.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" href="ElasticNet.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+<body class="wy-body-for-nav"> 
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+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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+      current_version = "2025.1"
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+      data-bs-toggle="dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
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+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/lasso_regression.ipynb.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Lasso-Regression-for-YearPredictionMSD-dataset">
+<h1>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Lasso-Regression-for-YearPredictionMSD-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset_dir</span> <span class="o">=</span> <span class="s2">&quot;data&quot;</span>
+<span class="n">dataset_name</span> <span class="o">=</span> <span class="s2">&quot;year_prediction_msd&quot;</span>
+<span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip&quot;</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">local_url</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">url</span><span class="p">))</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">local_url</span><span class="p">):</span>
+    <span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="s2">&quot;wb+&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">response</span><span class="o">.</span><span class="n">iter_content</span><span class="p">(</span><span class="mi">8192</span><span class="p">):</span>
+            <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="n">year</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((463810, 90), (51535, 90), (463810,), (51535,))
+</pre></div></div>
+</div>
+</section>
+<section id="Normalize-the-data">
+<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
+    <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Lasso</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.06 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 0.9676607251167297&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class Lasso</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Lasso</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 0.83 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Lasso algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 0.9676599502563477&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="ElasticNet.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="linear_regression.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
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+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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diff --git a/2025.1/samples/lasso_regression.ipynb b/2025.1/samples/lasso_regression.ipynb
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/2025.1/samples/lasso_regression.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
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+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/linear_regression.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Linear-Regression-for-YearPredictionMSD-dataset">
+<h1>Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Linear-Regression-for-YearPredictionMSD-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset_dir</span> <span class="o">=</span> <span class="s2">&quot;data&quot;</span>
+<span class="n">dataset_name</span> <span class="o">=</span> <span class="s2">&quot;year_prediction_msd&quot;</span>
+<span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip&quot;</span>
+
+<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">local_url</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">url</span><span class="p">))</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">local_url</span><span class="p">):</span>
+    <span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="s2">&quot;wb+&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">response</span><span class="o">.</span><span class="n">iter_content</span><span class="p">(</span><span class="mi">8192</span><span class="p">):</span>
+            <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="n">year</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">local_url</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">year</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((463810, 90), (51535, 90), (463810,), (51535,))
+</pre></div></div>
+</div>
+</section>
+<section id="Normalize-the-data">
+<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.03 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 0.7716818451881409&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class LinearRegression</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 0.53 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Linear Regression algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 0.7716856598854065&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="lasso_regression.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+  <div class="version-switcher__container dropdown">
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+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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diff --git a/2025.1/samples/linear_regression.ipynb b/2025.1/samples/linear_regression.ipynb
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/2025.1/samples/linear_regression.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/logistictic_regression_cifar.html b/2025.1/samples/logistictic_regression_cifar.html
new file mode 100644
index 0000000000..c9d50e95a0
--- /dev/null
+++ b/2025.1/samples/logistictic_regression_cifar.html
@@ -0,0 +1,693 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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+      <link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
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+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/doctools.js?v=9bcbadda"></script>
+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
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+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn SVC for Adult dataset" href="svc_adult.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="knn_mnist.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+      </script>
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+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+        <div class="wy-side-nav-search" >
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+          
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+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/logistictic_regression_cifar.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Logistic-Regression-for-Cifar-dataset">
+<h1>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Logistic-Regression-for-Cifar-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+
+<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;CIFAR-100&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">43</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((54000, 3072), (6000, 3072), (54000,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
+    <span class="s2">&quot;solver&quot;</span><span class="p">:</span> <span class="s2">&quot;lbfgs&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;multi_class&quot;</span><span class="p">:</span> <span class="s2">&quot;multinomial&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 24.82 s&#39;
+</pre></div></div>
+</div>
+<p>Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">log_loss_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">log_loss</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn Log Loss: </span><span class="si">{</span><span class="n">log_loss_opt</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class LogisticRegression</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 395.03 s&#39;
+</pre></div></div>
+</div>
+<p>Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">log_loss_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">log_loss</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn Log Loss: </span><span class="si">{</span><span class="n">log_loss_original</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn Log Loss: 3.7140870590578428 s&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare Log Loss metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Log Loss metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">log_loss_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Log Loss metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">log_loss_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">log_loss_opt</span><span class="o">/</span><span class="n">log_loss_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="knn_mnist.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="svc_adult.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn SVC for Adult dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
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+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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diff --git a/2025.1/samples/logistictic_regression_cifar.ipynb b/2025.1/samples/logistictic_regression_cifar.ipynb
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/2025.1/samples/logistictic_regression_cifar.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/nusvr_medical_charges.html b/2025.1/samples/nusvr_medical_charges.html
new file mode 100644
index 0000000000..82c8dc7a8c
--- /dev/null
+++ b/2025.1/samples/nusvr_medical_charges.html
@@ -0,0 +1,702 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="index" title="Index" href="../genindex.html" />
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+    <link rel="next" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" href="random_forest_yolanda.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" href="linear_regression.html" />
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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+          
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+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/nusvr_medical_charges.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-NuSVR-for-Medical-Charges-dataset">
+<h1>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-NuSVR-for-Medical-Charges-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;medical_charges_nominal&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Encode categorical features</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cat_columns</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">([</span><span class="s2">&quot;category&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span>
+<span class="n">x</span><span class="p">[</span><span class="n">cat_columns</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">cat_columns</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">codes</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((48919, 11), (114146, 11), (48919,), (114146,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">NuSVR</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;nu&quot;</span><span class="p">:</span> <span class="mf">0.4</span><span class="p">,</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="n">y_train</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
+    <span class="s2">&quot;degree&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s2">&quot;kernel&quot;</span><span class="p">:</span> <span class="s2">&quot;poly&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">nusvr</span> <span class="o">=</span> <span class="n">NuSVR</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 24.69 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_opt</span> <span class="o">=</span> <span class="n">nusvr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn R2 score: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn R2 score: 0.8635974264586637&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class NuSVR</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">NuSVR</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">nusvr</span> <span class="o">=</span> <span class="n">NuSVR</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 331.85 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the NuSVR algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">score_original</span> <span class="o">=</span> <span class="n">nusvr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn R2 score: </span><span class="si">{</span><span class="n">score_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn R2 score: 0.8636031741516902&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare R2 score of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;R2 score of patched Scikit-learn: </span><span class="si">{</span><span class="n">score_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;R2 score of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">score_opt</span><span class="o">/</span><span class="n">score_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="linear_regression.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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diff --git a/2025.1/samples/nusvr_medical_charges.ipynb b/2025.1/samples/nusvr_medical_charges.ipynb
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/2025.1/samples/nusvr_medical_charges.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/random_forest_yolanda.html b/2025.1/samples/random_forest_yolanda.html
new file mode 100644
index 0000000000..cd4b96f9bb
--- /dev/null
+++ b/2025.1/samples/random_forest_yolanda.html
@@ -0,0 +1,684 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
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+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Random Forest for Yolanda dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/random_forest_yolanda.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Random-Forest-for-Yolanda-dataset">
+<h1>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Random-Forest-for-Yolanda-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Yolanda&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">72</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((280000, 100), (120000, 100), (280000,), (120000,))
+</pre></div></div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_estimators&quot;</span><span class="p">:</span> <span class="mi">150</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">44</span><span class="p">,</span> <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 42.56 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn Mean Squared Error: </span><span class="si">{</span><span class="n">mse_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class RandomForestRegressor.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 123.34 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Random Forest algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn Mean Squared Error: </span><span class="si">{</span><span class="n">mse_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn Mean Squared Error: 83.62232345666878&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_opt</span><span class="o">/</span><span class="n">mse_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="nusvr_medical_charges.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="ridge_regression.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
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+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f04b66e84d0>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+          SphinxRtdTheme.Navigation.enable(true);
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+
+</body>
+</html>
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diff --git a/2025.1/samples/random_forest_yolanda.ipynb b/2025.1/samples/random_forest_yolanda.ipynb
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/2025.1/samples/random_forest_yolanda.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/ridge_regression.html b/2025.1/samples/ridge_regression.html
new file mode 100644
index 0000000000..810d18e34e
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+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../samples.html">Samples</a></li>
+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/samples/ridge_regression.ipynb.txt" rel="nofollow"> View page source</a>
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-Ridge-Regression-for-Airlines-DepDelay-dataset">
+<h1>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Ridge-Regression-for-Airlines-DepDelay-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Airlines_DepDelay_10M&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Preprocessing">
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
+<p>Let’s encode categorical features with LabelEncoder</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;UniqueCarrier&quot;</span><span class="p">,</span> <span class="s2">&quot;Origin&quot;</span><span class="p">,</span> <span class="s2">&quot;Dest&quot;</span><span class="p">]:</span>
+    <span class="n">le</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+    <span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+((9000000, 9), (1000000, 9), (9000000,), (1000000,))
+</pre></div></div>
+</div>
+<p>Normalize the data</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">StandardScaler</span>
+
+<span class="n">scaler_x</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">scaler_y</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_train</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">scaler_x</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_train</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+<span class="n">x_test</span> <span class="o">=</span> <span class="n">scaler_x</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+
+<span class="n">scaler_y</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
+<span class="n">y_train</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">scaler_y</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="mf">0.3</span><span class="p">,</span>
+    <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 0.06 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_opt</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Patched Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Patched Scikit-learn MSE: 1.0014288520708046&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class Ridge</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 0.70 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the Ridge Regression algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">mse_metric_original</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn MSE: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn MSE: 1.0014288520708057&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;Compare MSE metric of patched Scikit-learn and original&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of patched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;MSE metric of unpatched Scikit-learn: </span><span class="si">{</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Metrics ratio: </span><span class="si">{</span><span class="n">mse_metric_opt</span><span class="o">/</span><span class="n">mse_metric_original</span><span class="si">}</span><span class="s2"> &lt;br&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h3&gt;With Scikit-learn-intelex patching you can:&lt;/h3&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="random_forest_yolanda.html" class="btn btn-neutral float-left" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="kmeans.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+          SphinxRtdTheme.Navigation.enable(true);
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diff --git a/2025.1/samples/ridge_regression.ipynb b/2025.1/samples/ridge_regression.ipynb
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/2025.1/samples/ridge_regression.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/svc_adult.html b/2025.1/samples/svc_adult.html
new file mode 100644
index 0000000000..3282b5a4eb
--- /dev/null
+++ b/2025.1/samples/svc_adult.html
@@ -0,0 +1,689 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn SVC for Adult dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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+
+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="../_static/documentation_options.js?v=aa5d0537"></script>
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+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
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+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" href="ElasticNet.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset" href="logistictic_regression_cifar.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+          
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+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn SVC for Adult dataset</li>
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+             
+  <section id="Intel®-Extension-for-Scikit-learn-SVC-for-Adult-dataset">
+<h1>Intel® Extension for Scikit-learn SVC for Adult dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-SVC-for-Adult-dataset" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+</pre></div>
+</div>
+</div>
+<section id="Download-the-data">
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;a9a&quot;</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Split the data into train and test sets</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">100.0</span><span class="p">,</span> <span class="s2">&quot;kernel&quot;</span><span class="p">:</span> <span class="s2">&quot;rbf&quot;</span><span class="p">,</span> <span class="s2">&quot;gamma&quot;</span><span class="p">:</span> <span class="s2">&quot;scale&quot;</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_patched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">train_patched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Intel® extension for Scikit-learn time: 14.08 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for Intel® extension for Scikit-learn SVC:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for Intel® extension for Scikit-learn SVC:
+              precision    recall  f1-score   support
+
+        -1.0       0.87      0.90      0.88      7414
+         1.0       0.64      0.58      0.61      2355
+
+    accuracy                           0.82      9769
+   macro avg       0.76      0.74      0.75      9769
+weighted avg       0.82      0.82      0.82      9769
+
+
+</pre></div></div>
+</div>
+<p><em>The first column of the classification report above is the class labels.</em></p>
+</section>
+<section id="Train-the-same-algorithm-with-original-Scikit-learn">
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
+<p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class SVC.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training of the SVC algorithm with original Scikit-learn library for Adult dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
+
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">train_unpatched</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+<span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">train_unpatched</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Original Scikit-learn time: 803.06 s&#39;
+</pre></div></div>
+</div>
+<p>Predict and get a result of the SVC algorithm with original Scikit-learn</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+<span class="n">report</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Classification report for original Scikit-learn SVC:</span><span class="se">\n</span><span class="si">{</span><span class="n">report</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Classification report for original Scikit-learn SVC:
+              precision    recall  f1-score   support
+
+        -1.0       0.87      0.90      0.88      7414
+         1.0       0.64      0.58      0.61      2355
+
+    accuracy                           0.82      9769
+   macro avg       0.76      0.74      0.75      9769
+weighted avg       0.82      0.82      0.82      9769
+
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">HTML</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;h2&gt;With scikit-learn-intelex patching you can:&lt;/h2&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;ul&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Fast execution training and prediction of Scikit-learn models;&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get the similar quality&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;li&gt;Get speedup in &lt;strong&gt;</span><span class="si">{</span><span class="p">(</span><span class="n">train_unpatched</span><span class="o">/</span><span class="n">train_patched</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&lt;/strong&gt; times.&lt;/li&gt;&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;&lt;/ul&gt;&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul></div>
+</div>
+</section>
+</section>
+
+
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+      data-bs-toggle="dropdown"
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diff --git a/2025.1/samples/svc_adult.ipynb b/2025.1/samples/svc_adult.ipynb
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/2025.1/samples/svc_adult.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/samples/tsne.html b/2025.1/samples/tsne.html
new file mode 100644
index 0000000000..00cb1ea167
--- /dev/null
+++ b/2025.1/samples/tsne.html
@@ -0,0 +1,669 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Intel® Extension for Scikit-learn TSNE example &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
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+      <link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
+  
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="../_static/documentation_options.js?v=aa5d0537"></script>
+      <script src="../_static/doctools.js?v=9bcbadda"></script>
+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Kaggle Kernels" href="../kaggle.html" />
+    <link rel="prev" title="Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset" href="dbscan.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
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+        // Load TMS
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+      </script>
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+</head>
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+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
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+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
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+      <li class="breadcrumb-item active">Intel® Extension for Scikit-learn TSNE example</li>
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+            <a href="../_sources/samples/tsne.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Intel®-Extension-for-Scikit-learn-TSNE-example">
+<h1>Intel® Extension for Scikit-learn TSNE example<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-TSNE-example" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Generate-the-data">
+<h2>Generate the data<a class="headerlink" href="#Generate-the-data" title="Link to this heading"></a></h2>
+<div class="line-block">
+<div class="line">Generate isotropic Gaussian blobs for clustering. With the number of samples: 20k Number of features: 100 Number of blobs: 4 Source:</div>
+<div class="line"><a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html">https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html</a></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">20000</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
+</pre></div></div>
+</div>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">TSNE</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_components&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">tsne</span> <span class="o">=</span> <span class="n">TSNE</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+<span class="n">embedding_intelex</span> <span class="o">=</span> <span class="n">tsne</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">time_opt</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® extension for Scikit-learn time: </span><span class="si">{</span><span class="n">time_opt</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intel® Extension for scikit-learn. Divergence: </span><span class="si">{</span><span class="n">tsne</span><span class="o">.</span><span class="n">kl_divergence_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Intel® extension for Scikit-learn time: 12.63 s
+Intel® Extension for scikit-learn. Divergence: 4.289110606110757
+</pre></div></div>
+</div>
+<p>### Train the same algorithm with original Scikit-learn In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class TSNE.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+
+<span class="n">unpatch_sklearn</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Training algorithm with original Scikit-learn library for generated dataset</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">TSNE</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;n_components&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">}</span>
+<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+<span class="n">tsne</span> <span class="o">=</span> <span class="n">TSNE</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+<span class="n">embedding_original</span> <span class="o">=</span> <span class="n">tsne</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">time_original</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn time: </span><span class="si">{</span><span class="n">time_original</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original Scikit-learn. Divergence: </span><span class="si">{</span><span class="n">tsne</span><span class="o">.</span><span class="n">kl_divergence_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Original Scikit-learn time: 37.66 s
+Original Scikit-learn. Divergence: 4.2955403327941895
+</pre></div></div>
+</div>
+</section>
+<section id="Plot-embeddings-original-scikit-learn-and-Intel®-extension">
+<h2>Plot embeddings original scikit-learn and Intel® extension<a class="headerlink" href="#Plot-embeddings-original-scikit-learn-and-Intel®-extension" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">y</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">emb</span><span class="p">,</span> <span class="n">title</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+    <span class="p">[</span><span class="n">embedding_intelex</span><span class="p">,</span> <span class="n">embedding_original</span><span class="p">],</span>
+    <span class="p">[</span><span class="s2">&quot;Intel® Extension for scikit-learn&quot;</span><span class="p">,</span> <span class="s2">&quot;Original scikit-learn&quot;</span><span class="p">],</span>
+<span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">emb</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">emb</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">colors</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_tsne_15_0.png" src="../_images/samples_tsne_15_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/samples_tsne_15_1.png" src="../_images/samples_tsne_15_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sa">f</span><span class="s2">&quot;Speedup for this run: </span><span class="si">{</span><span class="p">(</span><span class="n">time_original</span><span class="o">/</span><span class="n">time_opt</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;Speedup for this run: 3.0&#39;
+</pre></div></div>
+</div>
+</section>
+</section>
+
+
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+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+      data-bs-toggle="dropdown"
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diff --git a/2025.1/samples/tsne.ipynb b/2025.1/samples/tsne.ipynb
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/2025.1/samples/tsne.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025.1/search.html b/2025.1/search.html
new file mode 100644
index 0000000000..9fcd70c19f
--- /dev/null
+++ b/2025.1/search.html
@@ -0,0 +1,498 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Search &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
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+      <link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
+
+  
+    <link rel="shortcut icon" href="_static/favicons.png"/>
+    
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="_static/documentation_options.js?v=aa5d0537"></script>
+      <script src="_static/doctools.js?v=9bcbadda"></script>
+      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="_static/copybutton.js?v=f281be69"></script>
+      <script src="_static/version_switcher.js?v=107152b5"></script>
+      <script src="_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <script src="_static/searchtools.js"></script>
+    <script src="_static/language_data.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="#" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
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+            var url = 'https://www.intel.com/content/dam/www/global/wap/tms-loader.js'; // WAP file URL
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="#" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
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+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
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+            Intel(R) Extension for Scikit-learn*
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<h1>Intel(R) Extension for Scikit-learn Support<a class="headerlink" href="#intel-r-extension-for-scikit-learn-support" title="Link to this heading"></a></h1>
+<p>We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn.</p>
+<p>Use the following methods if you face any challenges.</p>
+<section id="issues">
+<h2>Issues<a class="headerlink" href="#issues" title="Link to this heading"></a></h2>
+<p>If you have a problem, check out the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">GitHub Issues</a> to see if the issue you want to address is already reported.</p>
+<p>You may find users that have encountered the same bug or have similar ideas for changes or updates.</p>
+<p>You can use issues to report a problem, make a feature request, or add comments on an existing issue.</p>
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+            Intel(R) Extension for Scikit-learn*
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+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
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+            <a href="_sources/tutorials.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="intelex-tutorials-case-studies">
+<h1>Intel® Extension for Scikit-learn* Tutorials &amp; Case Studies<a class="headerlink" href="#intelex-tutorials-case-studies" title="Link to this heading"></a></h1>
+<p>Explore the tutorials &amp; case studies to help you to harness the full potential of Intel® Extension for Scikit-learn*.</p>
+<section id="tutorials">
+<h2>Tutorials<a class="headerlink" href="#tutorials" title="Link to this heading"></a></h2>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
+<div class="sd-row sd-row-cols-3 sd-row-cols-xs-3 sd-row-cols-sm-3 sd-row-cols-md-3 sd-row-cols-lg-3 docutils">
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Introduction to scikit-learn* Essentials for Machine Learning</div>
+<p class="sd-card-text">An overview of scikit-learn essentials.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/introduction-to-scikit-learn-essentials-for-ml.html"><span>https://www.intel.com/content/www/us/en/developer/videos/introduction-to-scikit-learn-essentials-for-ml.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Advanced scikit-learn* Essentials for Machine Learning</div>
+<p class="sd-card-text">Special technique to perform scikit-learn computation on Intel GPUs.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/advanced-scikit-learn-essentials-for-ml.html"><span>https://www.intel.com/content/www/us/en/developer/videos/advanced-scikit-learn-essentials-for-ml.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Develop Efficient AI Solutions with Accelerated Machine Learning</div>
+<p class="sd-card-text">Techniques for maximizing Intel® Extension for Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/accelerated-machine-learning-for-ai-solutions.html"><span>https://www.intel.com/content/www/us/en/developer/videos/accelerated-machine-learning-for-ai-solutions.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Getting started with classical Machine Learning Frameworks using Google Colaboratory</div>
+<p class="sd-card-text">Simple Installation of Intel® Extension for Scikit-learn* on Google Colaboratory.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Getting-started-with-classical-Machine-Learning-Frameworks-using/post/1450139"><span>https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Getting-started-with-classical-Machine-Learning-Frameworks-using/post/1450139</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Accelerate Machine Learning Workloads: K-means and GPairs Algorithms</div>
+<p class="sd-card-text">An introduction to Intel® Extension for Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/accelerate-ml-workloads-k-means-gpairs-algorithms.html"><span>https://www.intel.com/content/www/us/en/developer/videos/accelerate-ml-workloads-k-means-gpairs-algorithms.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Benchmarking Intel® Extension for Scikit-learn*: How Much Faster Is It?</div>
+<p class="sd-card-text">Explore and compare the performance of the Intel Extension for Scikit-learn* vs. stock Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/technical/benchmarking-intel-extension-for-scikit-learn.html"><span>https://www.intel.com/content/www/us/en/developer/articles/technical/benchmarking-intel-extension-for-scikit-learn.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Drive Innovation &amp; Performance into Your scikit-learn* Machine Learning Tasks</div>
+<p class="sd-card-text">An overview of scikit-learn essentials.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/drive-innovation-performance-scikit-learn-ml-tasks.html"><span>https://www.intel.com/content/www/us/en/developer/videos/drive-innovation-performance-scikit-learn-ml-tasks.html</span></a></div>
+</div>
+</div>
+</div>
+</section>
+<section id="case-studies">
+<h2>Case Studies<a class="headerlink" href="#case-studies" title="Link to this heading"></a></h2>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
+<div class="sd-row sd-row-cols-3 sd-row-cols-xs-3 sd-row-cols-sm-3 sd-row-cols-md-3 sd-row-cols-lg-3 docutils">
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Greener Machine Learning Computing with Intel® AI Acceleration</div>
+<p class="sd-card-text">Lowering the Environmental Impact of AI Workloads by 7x using Intel® Extension for Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/case-study/greener-machine-learning-with-intel-ai.html"><span>https://www.intel.com/content/www/us/en/developer/articles/case-study/greener-machine-learning-with-intel-ai.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Supply Chain Optimization at Enterprise Scale</div>
+<p class="sd-card-text">Leveraging Open-Source AI Technologies with Red Hat OpenShift* Data Science and Intel® Architecture.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/technical/supply-chain-optimization-at-enterprise-scale.html"><span>https://www.intel.com/content/www/us/en/developer/articles/technical/supply-chain-optimization-at-enterprise-scale.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+HippoScreen Improves AI Performance by 2.4x with oneAPI Tools</div>
+<p class="sd-card-text">The neurotechnology startup used Intel® Extension for Scikit-learn* to the improve efficiency and build times of deep-learning models used in its Brain Waves AI system.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html"><span>https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html</span></a></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
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+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
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+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
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+          <a href="index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Verbose Mode</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/verbose.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="verbose-mode">
+<span id="verbose"></span><h1>Verbose Mode<a class="headerlink" href="#verbose-mode" title="Link to this heading"></a></h1>
+<p>Use Intel® Extension for Scikit-learn* in verbose mode to find out which implementation of the algorithm is currently used,
+Intel® Extension for Scikit-learn* or original Scikit-learn.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Verbose mode is only available for <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">supported algorithms</span></a>.</p>
+</div>
+<p>To enable verbose mode, set the <code class="docutils literal notranslate"><span class="pre">SKLEARNEX_VERBOSE</span></code> environment variable as shown below:</p>
+<ul>
+<li><p>On Linux* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
+</pre></div>
+</div>
+</li>
+<li><p>On Windows* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
+</pre></div>
+</div>
+</li>
+</ul>
+<p>Alternatively, get Intel® Extension for Scikit-learn* logger and set its logging level in the Python code:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span>
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s1">&#39;sklearnex&#39;</span><span class="p">)</span>
+<span class="n">logger</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>During the calls that use Intel-optimized scikit-learn, you will receive additional print statements
+that indicate which implementation is being called.
+These print statements are only available for <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">supported algorithms</span></a>.</p>
+<p>For example, for DBSCAN you get one of these print statements depending on which implementation is used:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">INFO</span><span class="p">:</span><span class="n">sklearnex</span><span class="p">:</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">DBSCAN</span><span class="o">.</span><span class="n">fit</span><span class="p">:</span> <span class="n">running</span> <span class="n">accelerated</span> <span class="n">version</span> <span class="n">on</span> <span class="n">CPU</span>
+</pre></div>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">INFO</span><span class="p">:</span><span class="n">sklearnex</span><span class="p">:</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">DBSCAN</span><span class="o">.</span><span class="n">fit</span><span class="p">:</span> <span class="n">fallback</span> <span class="n">to</span> <span class="n">original</span> <span class="n">Scikit</span><span class="o">-</span><span class="n">learn</span>
+</pre></div>
+</div>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="array_api.html" class="btn btn-neutral float-left" title="Array API support" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 3a319e52f99dc3b89d49d5f4c412bd8e
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38637349",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>14.2</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb b/latest/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
new file mode 100644
index 0000000000..9336772cb3
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/daal4py_data_science.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilizing daal4py in Data Science Workflows\n",
+    "\n",
+    "The notebook below has been made to demonstrate daal4py in a data science context.  It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the absence of a power meter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.8.10 (default, May 19 2021, 18:05:58) \\n[GCC 7.3.0]'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob\n",
+    "import sys\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example will be exploring workout data pulled from Strava, processed into a CSV for Pandas and daal4py usage.  Below, we utilize pandas to read in the CSV file, and look at the head of dataframe with .head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>cadence</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>power</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.46</td>\n",
+       "      <td>81</td>\n",
+       "      <td>30.313309</td>\n",
+       "      <td>-97.732711</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.459</td>\n",
+       "      <td>2016-10-20T22:01:26.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185.800003</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.17</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313277</td>\n",
+       "      <td>-97.732715</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.710</td>\n",
+       "      <td>2016-10-20T22:01:27.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186.399994</td>\n",
+       "      <td>38</td>\n",
+       "      <td>11.04</td>\n",
+       "      <td>82</td>\n",
+       "      <td>30.313243</td>\n",
+       "      <td>-97.732717</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.874</td>\n",
+       "      <td>2016-10-20T22:01:28.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>186.800003</td>\n",
+       "      <td>38</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313212</td>\n",
+       "      <td>-97.732720</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.135</td>\n",
+       "      <td>2016-10-20T22:01:29.000Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>186.600006</td>\n",
+       "      <td>38</td>\n",
+       "      <td>19.43</td>\n",
+       "      <td>83</td>\n",
+       "      <td>30.313172</td>\n",
+       "      <td>-97.732723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.250</td>\n",
+       "      <td>2016-10-20T22:01:30.000Z</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     altitude  cadence  distance  hr   latitude  longitude  power  speed  \\\n",
+       "0  185.800003       51      3.46  81  30.313309 -97.732711     45  3.459   \n",
+       "1  185.800003       68      7.17  82  30.313277 -97.732715      0  3.710   \n",
+       "2  186.399994       38     11.04  82  30.313243 -97.732717     42  3.874   \n",
+       "3  186.800003       38     15.18  83  30.313212 -97.732720      5  4.135   \n",
+       "4  186.600006       38     19.43  83  30.313172 -97.732723      1  4.250   \n",
+       "\n",
+       "                       time  \n",
+       "0  2016-10-20T22:01:26.000Z  \n",
+       "1  2016-10-20T22:01:27.000Z  \n",
+       "2  2016-10-20T22:01:28.000Z  \n",
+       "3  2016-10-20T22:01:29.000Z  \n",
+       "4  2016-10-20T22:01:30.000Z  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workout_data_dd = pd.read_csv(\"data/cycling_dataset.csv\", index_col=0)\n",
+    "workout_data_dd.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data above has several key features that would be of great use here.  \n",
+    "- Altitude can affect performance, so it might be a useful feature.  \n",
+    "- Cadence is the revolutions per minute of the crank, and may have possible influence.  \n",
+    "- Heart Rate is a measure of the body's workout strain, and would have a high possibly of influence.\n",
+    "- Distance may have a loose correlation as it is highly route dependent, but might be possible.\n",
+    "- Speed has possible correlations as it ties directly into power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore and visualize some of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, we are trying to predict on the 'power' in Watts to see if we can generate a model that can predict one's power output without the usage of a cycling power meter.  Below are some basic scatterplots as we explore the data.  Scatterplots are great for looking for patterns and correlation in the data itself.  Below, we can see that cadence and speed are positively correlated.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "workout_data_dd.plot.scatter(\"cadence\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"hr\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"cadence\", \"speed\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"speed\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"altitude\", \"power\")\n",
+    "plt.show()\n",
+    "workout_data_dd.plot.scatter(\"distance\", \"power\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using daal4py for Machine Learning tasks\n",
+    "\n",
+    "In the sections below, we will be using daal4py directly.  After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py's training and prediction classes to generate a workable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import daal4py as d4p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is now the time to split the dataset into train and test sets.  This is demonstrated below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3902, 9)\n",
+      "(3000, 9) (902, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(workout_data_dd.shape)\n",
+    "train_set = workout_data_dd[0:3000]\n",
+    "test_set = workout_data_dd[3000:]\n",
+    "print(train_set.shape, test_set.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce the dataset, create X.  We drop the target, and other non-essential features.\n",
+    "reduced_dataset = train_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)\n",
+    "# Get the target, create Y\n",
+    "target = train_set.power.values.reshape((-1, 1))\n",
+    "# This is essentially doing np.array(dataset.power.values, ndmin=2).T\n",
+    "# as it needs to force a 2 dimensional array as we only have 1 target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "X is 5 features by 3k rows, Y is 3k rows by 1 column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3000, 5) (3000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(reduced_dataset.values.shape, target.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the Linear Regression Model, and train the model with the data.  We utilize daal4py's linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d4p_lm = d4p.linear_regression_training(interceptFlag=True)\n",
+    "lm_trained = d4p_lm.compute(reduced_dataset.values, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has this number of features:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model has this number of features: \", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction (inference) with the trained model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is trained, we can test it with the test part of the dataset.  We drop the same features to match that of the trained model, and put it into daal4py's linear_regression_prediction class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = test_set.drop([\"time\", \"power\", \"latitude\", \"longitude\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the Prediction object and use the reduced dataset for prediction.  The class's arguments use the independent data and the trained model from above as the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm_predictor_component = d4p.linear_regression_prediction()\n",
+    "result = lm_predictor_component.compute(subset.values, lm_trained.model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(result.prediction[0:300])\n",
+    "plt.plot(test_set.power.values[0:300])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph above shows the Orange (predicted) result over the Blue (original data).  This data is notoriously sparse in features leading to a difficult to predict target!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model properties\n",
+    "Another aspect of the model is the trained model's properties, which are explored below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Betas: [[ 1.51003501e+01 -1.25075548e-01  1.32249115e+00  1.64363922e-03\n",
+      "   8.53155955e-01 -1.09595022e+01]]\n",
+      "Number of betas: 6\n",
+      "Number of Features: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Betas:\", lm_trained.model.Beta)\n",
+    "print(\"Number of betas:\", lm_trained.model.NumberOfBetas)\n",
+    "print(\"Number of Features:\", lm_trained.model.NumberOfFeatures)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional metrics\n",
+    "We can generate metrics on the independent data with daal4py's low_order_moments() class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.90063975e+01, 3.75882355e+01, 4.98258371e+03, 2.41394741e+01,\n",
+       "        1.81623064e+00]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_processor = d4p.low_order_moments()\n",
+    "data = metrics_processor.compute(reduced_dataset.values)\n",
+    "data.standardDeviation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Migrating the trained model for inference on external systems\n",
+    "\n",
+    "Occasionally one may need to migrate the trained model to another system for inference only--this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"wb\") as model_pi:\n",
+    "    pickle.dump(lm_trained.model, model_pi)\n",
+    "    model_pi.close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trained model file above can be moved to an inference-only or embedded system.  This is useful if the training is extreamly heavy or computed-limited.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"trained_model2.pickle\", \"rb\") as model_import:\n",
+    "    lm_import = pickle.load(model_import)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The imported model from file is now usable again.  We can check the betas from the model to ensure that the trained model is present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.51003501e+01, -1.25075548e-01,  1.32249115e+00,\n",
+       "         1.64363922e-03,  8.53155955e-01, -1.09595022e+01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm_import.Beta"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8837a6bc722950b4562ef1f8ddb3cf1e2be71cad9580dda11136095ace1c488e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/latest/.doctrees/nbsphinx/samples/dbscan.ipynb b/latest/.doctrees/nbsphinx/samples/dbscan.ipynb
new file mode 100644
index 0000000000..d6e5c92653
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/dbscan.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import davies_bouldin_score\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fd95eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33da61da",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 6.37 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "params = {\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 469.21 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "start = timer()\n",
+    "y_pred = DBSCAN(**params).fit_predict(x_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = davies_bouldin_score(x_train, y_pred)\n",
+    "f\"Original Scikit-learn Davies-Bouldin score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848 <br>Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848 <br>Metrics ratio: 1.0 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>73.6</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/kmeans.ipynb b/latest/.doctrees/nbsphinx/samples/kmeans.ipynb
new file mode 100644
index 0000000000..df09f8ded5
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/kmeans.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be391256",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"spoken-arabic-digit\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdcb77d",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((236930, 14), (26326, 14), (236930,), (26326,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=123)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02a779e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ffc93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 7.36 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "params = {\n",
+    "    \"n_clusters\": 128,\n",
+    "    \"random_state\": 123,\n",
+    "    \"copy_x\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn inertia: 13346.641333761074\n",
+      "Intel® extension for Scikit-learn number of iterations: 274\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_opt = model.inertia_\n",
+    "n_iter_opt = model.n_iter_\n",
+    "print(f\"Intel® extension for Scikit-learn inertia: {inertia_opt}\")\n",
+    "print(f\"Intel® extension for Scikit-learn number of iterations: {n_iter_opt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the KMeans algorithm with original Scikit-learn library for spoken arabic digit dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 192.14 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "start = timer()\n",
+    "model = KMeans(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Let's take a look at inertia and number of iterations of the KMeans algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn inertia: 13352.813785961785\n",
+      "Original Scikit-learn number of iterations: 212\n"
+     ]
+    }
+   ],
+   "source": [
+    "inertia_original = model.inertia_\n",
+    "n_iter_original = model.n_iter_\n",
+    "print(f\"Original Scikit-learn inertia: {inertia_original}\")\n",
+    "print(f\"Original Scikit-learn number of iterations: {n_iter_original}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3639eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br><strong>Inertia:</strong><br>Patched Scikit-learn: 13346.641333761074 <br>Unpatched Scikit-learn: 13352.813785961785 <br>Ratio: 0.9995377414603653 <br><br><strong>Number of iterations:</strong><br>Patched Scikit-learn: 274 <br>Unpatched Scikit-learn: 212 <br>Ratio: 1.29 <br><br>Number of iterations is bigger but algorithm is much faster and inertia is lower<h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get speedup in <strong>26.1</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare inertia and number of iterations of patched Scikit-learn and original</h3><br>\"\n",
+    "    f\"<strong>Inertia:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {inertia_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {inertia_original} <br>\"\n",
+    "    f\"Ratio: {inertia_opt/inertia_original} <br><br>\"\n",
+    "    f\"<strong>Number of iterations:</strong><br>\"\n",
+    "    f\"Patched Scikit-learn: {n_iter_opt} <br>\"\n",
+    "    f\"Unpatched Scikit-learn: {n_iter_original} <br>\"\n",
+    "    f\"Ratio: {(n_iter_opt/n_iter_original):.2f} <br><br>\"\n",
+    "    f\"Number of iterations is bigger but algorithm is much faster and inertia is lower\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/knn_mnist.ipynb b/latest/.doctrees/nbsphinx/samples/knn_mnist.ipynb
new file mode 100644
index 0000000000..b8604d70f0
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/knn_mnist.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn KNN for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Download the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"mnist_784\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6259f584",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((56000, 784), (14000, 784), (56000,), (14000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 1.45 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "params = {\"n_neighbors\": 40, \"weights\": \"distance\", \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_opt = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ca549ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class KNeighborsClassifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training and predict KNN algorithm with original Scikit-learn library for MNSIT dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 36.15 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "knn = KNeighborsClassifier(**params).fit(x_train, y_train)\n",
+    "predicted = knn.predict(x_test)\n",
+    "time_original = timer() - start\n",
+    "f\"Original Scikit-learn time: {time_original:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33da9fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn KNN:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98      1365\n",
+      "           1       0.93      0.99      0.96      1637\n",
+      "           2       0.99      0.94      0.96      1401\n",
+      "           3       0.96      0.95      0.96      1455\n",
+      "           4       0.98      0.96      0.97      1380\n",
+      "           5       0.95      0.95      0.95      1219\n",
+      "           6       0.96      0.99      0.97      1317\n",
+      "           7       0.94      0.95      0.95      1420\n",
+      "           8       0.99      0.90      0.94      1379\n",
+      "           9       0.92      0.94      0.93      1427\n",
+      "\n",
+      "    accuracy                           0.96     14000\n",
+      "   macro avg       0.96      0.96      0.96     14000\n",
+      "weighted avg       0.96      0.96      0.96     14000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn KNN:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>24.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(time_original/time_opt):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/lasso_regression.ipynb b/latest/.doctrees/nbsphinx/samples/lasso_regression.ipynb
new file mode 100644
index 0000000000..967d0d4e54
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/lasso_regression.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import warnings\n",
+    "import os\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db2d1c39",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e58a6e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532874ab",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.01,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.9676607251167297'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Lasso algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.83 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "start = timer()\n",
+    "model = Lasso(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Lasso algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.9676599502563477'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13c86289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.9676607251167297 <br>MSE metric of unpatched Scikit-learn: 0.9676599502563477 <br>Metrics ratio: 1.0000008344650269 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.7</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/linear_regression.ipynb b/latest/.doctrees/nbsphinx/samples/linear_regression.ipynb
new file mode 100644
index 0000000000..508ee06d8c
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/linear_regression.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import requests\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad7ce109",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "801ea6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = \"data\"\n",
+    "dataset_name = \"year_prediction_msd\"\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip\"\n",
+    "\n",
+    "os.makedirs(dataset_dir, exist_ok=True)\n",
+    "local_url = os.path.join(dataset_dir, os.path.basename(url))\n",
+    "\n",
+    "if not os.path.isfile(local_url):\n",
+    "    response = requests.get(url, stream=True)\n",
+    "    with open(local_url, \"wb+\") as file:\n",
+    "        for data in response.iter_content(8192):\n",
+    "            file.write(data)\n",
+    "\n",
+    "year = pd.read_csv(local_url, header=None)\n",
+    "x = year.iloc[:, 1:].to_numpy(dtype=np.float32)\n",
+    "y = year.iloc[:, 0].to_numpy(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03431aec",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((463810, 90), (51535, 90), (463810,), (51535,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "### Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train.reshape(-1, 1))\n",
+    "y_train = scaler_y.transform(y_train.reshape(-1, 1)).ravel()\n",
+    "y_test = scaler_y.transform(y_test.reshape(-1, 1)).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.03 s'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "params = {\"n_jobs\": -1, \"copy_X\": False}\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 0.7716818451881409'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Linear Regression algorithm with original Scikit-learn library for YearPredictionMSD dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.53 s'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "start = timer()\n",
+    "model = LinearRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Linear Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 0.7716856598854065'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "91fb14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.7716818451881409 <br>MSE metric of unpatched Scikit-learn: 0.7716856598854065 <br>Metrics ratio: 0.9999950528144836 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>18.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb b/latest/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
new file mode 100644
index 0000000000..43727804d7
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/logistictic_regression_cifar.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb52aca",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "x, y = fetch_openml(name=\"CIFAR-100\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8ba7c8",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((54000, 3072), (6000, 3072), (54000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)\n",
+    "x_train.shape, x_test.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.82 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "params = {\n",
+    "    \"C\": 0.1,\n",
+    "    \"solver\": \"lbfgs\",\n",
+    "    \"multi_class\": \"multinomial\",\n",
+    "    \"n_jobs\": -1,\n",
+    "}\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Log Loss: 3.7073530800931587 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_opt = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Intel® extension for Scikit-learn Log Loss: {log_loss_opt} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the Logistic Regression algorithm with original Scikit-learn library for CIFAR dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 395.03 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38dfb5",
+   "metadata": {},
+   "source": [
+    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Log Loss: 3.7140870590578428 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = classifier.predict_proba(x_test)\n",
+    "log_loss_original = metrics.log_loss(y_test, y_predict)\n",
+    "f\"Original Scikit-learn Log Loss: {log_loss_original} s\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7d17e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss metric of patched Scikit-learn: 3.7073530800931587 <br>Log Loss metric of unpatched Scikit-learn: 3.7140870590578428 <br>Metrics ratio: 0.9981869086917978 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>15.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare Log Loss metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"Log Loss metric of patched Scikit-learn: {log_loss_opt} <br>\"\n",
+    "    f\"Log Loss metric of unpatched Scikit-learn: {log_loss_original} <br>\"\n",
+    "    f\"Metrics ratio: {log_loss_opt/log_loss_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb b/latest/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
new file mode 100644
index 0000000000..8c72c1b71d
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/nusvr_medical_charges.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf9ffe9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a9b315cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fbf604",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fafea10b",
+   "metadata": {},
+   "source": [
+    "Encode categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f77c30f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_columns = x.select_dtypes([\"category\"]).columns\n",
+    "x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8d3b6d",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((48919, 11), (114146, 11), (48919,), (114146,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 24.69 s'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "params = {\n",
+    "    \"nu\": 0.4,\n",
+    "    \"C\": y_train.mean(),\n",
+    "    \"degree\": 2,\n",
+    "    \"kernel\": \"poly\",\n",
+    "}\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_opt = nusvr.score(x_test, y_test)\n",
+    "f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 331.85 s'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import NuSVR\n",
+    "\n",
+    "start = timer()\n",
+    "nusvr = NuSVR(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b8faa6",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the NuSVR algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn R2 score: 0.8636031741516902'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_original = nusvr.score(x_test, y_test)\n",
+    "f\"Original Scikit-learn R2 score: {score_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a704d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
+    "    f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
+    "    f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
+    "    f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb b/latest/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
new file mode 100644
index 0000000000..276284ed9b
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/random_forest_yolanda.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from IPython.display import HTML\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d0b6bb9",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b3a2483",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((280000, 100), (120000, 100), (280000,), (120000,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fecbbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 42.56 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9279181",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d05bc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76a8d5f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 123.34 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "start = timer()\n",
+    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f162fe6b",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d5b5e45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(x_test)\n",
+    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
+    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e255e563",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/ridge_regression.ipynb b/latest/.doctrees/nbsphinx/samples/ridge_regression.ipynb
new file mode 100644
index 0000000000..1c159a13ae
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/ridge_regression.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1b922d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import warnings\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1a9234",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e73dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f852cad8",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27ebb377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
+    "    le = LabelEncoder().fit(x[col])\n",
+    "    x[col] = le.transform(x[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147b3e82",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d332789",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
+    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246f819f",
+   "metadata": {},
+   "source": [
+    "Normalize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "454a341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "scaler_x = MinMaxScaler()\n",
+    "scaler_y = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df400504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
+    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
+    "\n",
+    "scaler_x.fit(x_train)\n",
+    "x_train = scaler_x.transform(x_train)\n",
+    "x_test = scaler_x.transform(x_test)\n",
+    "\n",
+    "scaler_y.fit(y_train)\n",
+    "y_train = scaler_y.transform(y_train).ravel()\n",
+    "y_test = scaler_y.transform(y_test).ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1d4fac",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0014288520708046'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe6db0d",
+   "metadata": {},
+   "source": [
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f64ba97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f242c6da",
+   "metadata": {},
+   "source": [
+    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67243849",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 0.70 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "start = timer()\n",
+    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0014288520708057'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict = model.predict(x_test)\n",
+    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
+    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bde360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>10.9</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
+    "    f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
+    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
+    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_original} <br>\"\n",
+    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/svc_adult.ipynb b/latest/.doctrees/nbsphinx/samples/svc_adult.ipynb
new file mode 100644
index 0000000000..9e49bcfecd
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/svc_adult.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn SVC for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from IPython.display import HTML\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cdcbfa6",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"a9a\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a6df301",
+   "metadata": {},
+   "source": [
+    "Split the data into train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96e14dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with Intel® Extension for Scikit-learn for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 14.08 s'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "params = {\"C\": 100.0, \"kernel\": \"rbf\", \"gamma\": \"scale\"}\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_patched = timer() - start\n",
+    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d01cdabc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ead2a44",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for Intel® extension for Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for Intel® extension for Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "*The first column of the classification report above is the class labels.*  \n",
+    "  \n",
+    "### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class SVC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training of the SVC algorithm with original Scikit-learn library for Adult dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn time: 803.06 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "start = timer()\n",
+    "classifier = SVC(**params).fit(x_train, y_train)\n",
+    "train_unpatched = timer() - start\n",
+    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a7a747",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the SVC algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7644999d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report for original Scikit-learn SVC:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.87      0.90      0.88      7414\n",
+      "         1.0       0.64      0.58      0.61      2355\n",
+      "\n",
+      "    accuracy                           0.82      9769\n",
+      "   macro avg       0.76      0.74      0.75      9769\n",
+      "weighted avg       0.82      0.82      0.82      9769\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted = classifier.predict(x_test)\n",
+    "report = metrics.classification_report(y_test, predicted)\n",
+    "print(f\"Classification report for original Scikit-learn SVC:\\n{report}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc992182",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>57.0</strong> times.</li></ul>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    f\"<h2>With scikit-learn-intelex patching you can:</h2>\"\n",
+    "    f\"<ul>\"\n",
+    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
+    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
+    "    f\"<li>Get the similar quality</li>\"\n",
+    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
+    "    f\"</ul>\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/latest/.doctrees/nbsphinx/samples/tsne.ipynb b/latest/.doctrees/nbsphinx/samples/tsne.ipynb
new file mode 100644
index 0000000000..99ad8fcefd
--- /dev/null
+++ b/latest/.doctrees/nbsphinx/samples/tsne.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c4abc0",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn TSNE example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23512089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from timeit import default_timer as timer\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e359f6",
+   "metadata": {},
+   "source": [
+    "### Generate the data \n",
+    "Generate isotropic Gaussian blobs for clustering.\n",
+    "<br/>\n",
+    "With the number of samples: 20k<br/> \n",
+    "Number of features: 100 <br/>\n",
+    "Number of blobs: 4 <br/>\n",
+    "Source: <br/>    \n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27b99b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = make_blobs(n_samples=20000, centers=4, n_features=100, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0341cac9",
+   "metadata": {},
+   "source": [
+    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
+    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "244c5bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb14ac8",
+   "metadata": {},
+   "source": [
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "693b4e26",
+   "metadata": {},
+   "source": [
+    "Training TSNE algorithm with Intel® Extension for Scikit-learn for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9b8f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel® extension for Scikit-learn time: 12.63 s\n",
+      "Intel® Extension for scikit-learn. Divergence: 4.289110606110757\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_intelex = tsne.fit_transform(x)\n",
+    "time_opt = timer() - start\n",
+    "\n",
+    "print(f\"Intel® extension for Scikit-learn time: {time_opt:.2f} s\")\n",
+    "print(f\"Intel® Extension for scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8e7b0b",
+   "metadata": {},
+   "source": [
+    "  ### Train the same algorithm with original Scikit-learn\n",
+    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class TSNE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bb884d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearnex import unpatch_sklearn\n",
+    "\n",
+    "unpatch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfa0dba",
+   "metadata": {},
+   "source": [
+    "Training algorithm with original Scikit-learn library for generated dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae421d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Scikit-learn time: 37.66 s\n",
+      "Original Scikit-learn. Divergence: 4.2955403327941895\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "params = {\"n_components\": 2, \"random_state\": 42}\n",
+    "start = timer()\n",
+    "tsne = TSNE(**params)\n",
+    "embedding_original = tsne.fit_transform(x)\n",
+    "time_original = timer() - start\n",
+    "\n",
+    "print(f\"Original Scikit-learn time: {time_original:.2f} s\")\n",
+    "print(f\"Original Scikit-learn. Divergence: {tsne.kl_divergence_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8358d7c2",
+   "metadata": {},
+   "source": [
+    "### Plot embeddings original scikit-learn and Intel® extension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "43ab1a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [int(m) for m in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35147d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for emb, title in zip(\n",
+    "    [embedding_intelex, embedding_original],\n",
+    "    [\"Intel® Extension for scikit-learn\", \"Original scikit-learn\"],\n",
+    "):\n",
+    "    plt.scatter(emb[:, 0], emb[:, 1], c=colors)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"x\")\n",
+    "    plt.ylabel(\"y\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffd79e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Speedup for this run: 3.0'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Speedup for this run: {(time_original/time_opt):.1f}\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="/scikit-learn-intelex/kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="/scikit-learn-intelex/kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="/scikit-learn-intelex/array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
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+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
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+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
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+  
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
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+  <h1>All modules for which code is available</h1>
+<ul><li><a href="sklearnex/basic_statistics/basic_statistics.html">sklearnex.basic_statistics.basic_statistics</a></li>
+<li><a href="sklearnex/basic_statistics/incremental_basic_statistics.html">sklearnex.basic_statistics.incremental_basic_statistics</a></li>
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+<li><a href="sklearnex/linear_model/incremental_linear.html">sklearnex.linear_model.incremental_linear</a></li>
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+      data-bs-toggle="dropdown"
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+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../guide/acceleration.html">Tuning Guide</a><ul>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#code-style">Code Style</a></li>
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+          <a href="../../../index.html">Intel(R) Extension for Scikit-learn*</a>
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+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">sklearnex.basic_statistics.basic_statistics</li>
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+      </li>
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+             
+  <h1>Source code for sklearnex.basic_statistics.basic_statistics</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2023 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">_check_sample_weight</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.basic_statistics</span> <span class="kn">import</span> <span class="n">BasicStatistics</span> <span class="k">as</span> <span class="n">onedal_BasicStatistics</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">PatchingConditionsChain</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.6&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">validate_data</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">validate_data</span> <span class="o">=</span> <span class="n">BaseEstimator</span><span class="o">.</span><span class="n">_validate_data</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">StrOptions</span>
+
+
+<div class="viewcode-block" id="BasicStatistics">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics">[docs]</a>
+<span class="nd">@control_n_jobs</span><span class="p">(</span><span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;fit&quot;</span><span class="p">])</span>
+<span class="k">class</span> <span class="nc">BasicStatistics</span><span class="p">(</span><span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Estimator for basic statistics.</span>
+<span class="sd">    Allows to compute basic statistics for provided data.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    result_options: string or list, default=&#39;all&#39;</span>
+<span class="sd">        Used to set statistics to calculate. Possible values are ``&#39;min&#39;``, ``&#39;max&#39;``, ``&#39;sum&#39;``, ``&#39;mean&#39;``, ``&#39;variance&#39;``,</span>
+<span class="sd">        ``&#39;variation&#39;``, ``sum_squares&#39;``, ``sum_squares_centered&#39;``, ``&#39;standard_deviation&#39;``, ``&#39;second_order_raw_moment&#39;``</span>
+<span class="sd">        or a list containing any of these values. If set to ``&#39;all&#39;`` then all possible statistics will be</span>
+<span class="sd">        calculated.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">        min_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Minimum of each feature over all samples.</span>
+<span class="sd">        max_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Maximum of each feature over all samples.</span>
+<span class="sd">        sum_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of each feature over all samples.</span>
+<span class="sd">        mean_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Mean of each feature over all samples.</span>
+<span class="sd">        variance_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Variance of each feature over all samples.</span>
+<span class="sd">        variation_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Variation of each feature over all samples.</span>
+<span class="sd">        sum_squares_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of squares for each feature over all samples.</span>
+<span class="sd">        standard_deviation_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Standard deviation of each feature over all samples.</span>
+<span class="sd">        sum_squares_centered_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Centered sum of squares for each feature over all samples.</span>
+<span class="sd">        second_order_raw_moment_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Second order moment of each feature over all samples.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Attribute exists only if corresponding result option has been provided.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Names of attributes without the trailing underscore are</span>
+<span class="sd">    supported currently but deprecated in 2025.1 and will be removed in 2026.0</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Some results can exhibit small variations due to</span>
+<span class="sd">    floating point error accumulation and multithreading.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.basic_statistics import BasicStatistics</span>
+<span class="sd">    &gt;&gt;&gt; bs = BasicStatistics(result_options=[&#39;sum&#39;, &#39;min&#39;, &#39;max&#39;])</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; bs.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; bs.sum_</span>
+<span class="sd">    np.array([4., 6.])</span>
+<span class="sd">    &gt;&gt;&gt; bs.min_</span>
+<span class="sd">    np.array([1., 2.])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">result_options</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">=</span> <span class="n">result_options</span>
+
+    <span class="n">_onedal_basic_statistics</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_BasicStatistics</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="p">[</span>
+                <span class="n">StrOptions</span><span class="p">(</span>
+                    <span class="p">{</span>
+                        <span class="s2">&quot;all&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;min&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;max&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variance&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;standard_deviation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares_centered&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;second_order_raw_moment&quot;</span><span class="p">,</span>
+                    <span class="p">}</span>
+                <span class="p">),</span>
+                <span class="nb">list</span><span class="p">,</span>
+            <span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="nf">_save_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="n">onedal_BasicStatistics</span><span class="o">.</span><span class="n">get_all_result_options</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="nb">setattr</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">result_options</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span><span class="p">,</span>
+                <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="p">,</span> <span class="n">result_options</span><span class="p">),</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">option</span> <span class="ow">in</span> <span class="n">result_options</span><span class="p">:</span>
+                <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">option</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="p">,</span> <span class="n">option</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attr</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="n">onedal_BasicStatistics</span><span class="o">.</span><span class="n">get_all_result_options</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">result_options</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span>
+        <span class="n">is_deprecated_attr</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">attr</span> <span class="o">==</span> <span class="n">result_options</span><span class="p">)</span>
+        <span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">attr</span> <span class="ow">in</span> <span class="n">result_options</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">is_deprecated_attr</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Result attributes without a trailing underscore were deprecated in version 2025.1 and will be removed in 2026.0&quot;</span>
+            <span class="p">)</span>
+            <span class="n">attr</span> <span class="o">+=</span> <span class="s2">&quot;_&quot;</span>
+        <span class="k">if</span> <span class="n">attr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">attr</span><span class="p">]</span>
+
+        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;</span><span class="si">{</span><span class="n">attr</span><span class="si">}</span><span class="s2">&#39;&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearnex.basic_statistics.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span> <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">_check_sample_weight</span><span class="p">(</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span><span class="p">,</span>
+        <span class="p">}</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_basic_statistics</span><span class="p">(</span><span class="o">**</span><span class="n">onedal_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="p">,</span> <span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_save_attributes</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">1</span>
+
+<div class="viewcode-block" id="BasicStatistics.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calculate statistics of X.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Data for compute, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Weights for compute weighted statistics, where ``n_samples`` is the number of samples.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
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+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
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+</ul>
+</li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../blogs.html">Medium Blogs</a></li>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="../../../support.html#issues">Issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../contribute.html#code-style">Code Style</a></li>
+</ul>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../../index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <div class="wy-nav-content">
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+      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">sklearnex.basic_statistics.incremental_basic_statistics</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for sklearnex.basic_statistics.incremental_basic_statistics</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span><span class="p">,</span> <span class="n">gen_batches</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">_check_sample_weight</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.basic_statistics</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IncrementalBasicStatistics</span> <span class="k">as</span> <span class="n">onedal_IncrementalBasicStatistics</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">PatchingConditionsChain</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">Interval</span><span class="p">,</span> <span class="n">StrOptions</span>
+
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.6&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">validate_data</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">validate_data</span> <span class="o">=</span> <span class="n">BaseEstimator</span><span class="o">.</span><span class="n">_validate_data</span>
+
+
+<div class="viewcode-block" id="IncrementalBasicStatistics">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics">[docs]</a>
+<span class="nd">@control_n_jobs</span><span class="p">(</span><span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span> <span class="s2">&quot;_onedal_finalize_fit&quot;</span><span class="p">])</span>
+<span class="k">class</span> <span class="nc">IncrementalBasicStatistics</span><span class="p">(</span><span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates basic statistics on the given data, allows for computation when the data are split into</span>
+<span class="sd">    batches. The user can use ``partial_fit`` method to provide a single batch of data or use the ``fit`` method to provide</span>
+<span class="sd">    the entire dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    result_options: string or list, default=&#39;all&#39;</span>
+<span class="sd">        List of statistics to compute</span>
+
+<span class="sd">    batch_size : int, default=None</span>
+<span class="sd">        The number of samples to use for each batch. Only used when calling</span>
+<span class="sd">        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``</span>
+<span class="sd">        is inferred from the data and set to ``5 * n_features``.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">        min_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Minimum of each feature over all samples.</span>
+
+<span class="sd">        max_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Maximum of each feature over all samples.</span>
+
+<span class="sd">        sum_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of each feature over all samples.</span>
+
+<span class="sd">        mean_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Mean of each feature over all samples.</span>
+
+<span class="sd">        variance_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Variance of each feature over all samples.</span>
+
+<span class="sd">        variation_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Variation of each feature over all samples.</span>
+
+<span class="sd">        sum_squares_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Sum of squares for each feature over all samples.</span>
+
+<span class="sd">        standard_deviation_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Standard deviation of each feature over all samples.</span>
+
+<span class="sd">        sum_squares_centered_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Centered sum of squares for each feature over all samples.</span>
+
+<span class="sd">        second_order_raw_moment_ : ndarray of shape (n_features,)</span>
+<span class="sd">            Second order moment of each feature over all samples.</span>
+
+<span class="sd">        n_samples_seen_ : int</span>
+<span class="sd">            The number of samples processed by the estimator. Will be reset on</span>
+<span class="sd">            new calls to ``fit``, but increments across ``partial_fit`` calls.</span>
+
+<span class="sd">        batch_size_ : int</span>
+<span class="sd">            Inferred batch size from ``batch_size``.</span>
+
+<span class="sd">        n_features_in_ : int</span>
+<span class="sd">            Number of features seen during ``fit`` or  ``partial_fit``.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Attribute exists only if corresponding result option has been provided.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Names of attributes without the trailing underscore are</span>
+<span class="sd">    supported currently but deprecated in 2025.1 and will be removed in 2026.0</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.basic_statistics import IncrementalBasicStatistics</span>
+<span class="sd">    &gt;&gt;&gt; incbs = IncrementalBasicStatistics(batch_size=1)</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.partial_fit(X[:1])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.partial_fit(X[1:])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.sum_</span>
+<span class="sd">    np.array([4., 6.])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.min_</span>
+<span class="sd">    np.array([1., 2.])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; incbs.sum_</span>
+<span class="sd">    np.array([4., 6.])</span>
+<span class="sd">    &gt;&gt;&gt; incbs.max_</span>
+<span class="sd">    np.array([3., 4.])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_onedal_incremental_basic_statistics</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_IncrementalBasicStatistics</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="p">[</span>
+                <span class="n">StrOptions</span><span class="p">(</span>
+                    <span class="p">{</span>
+                        <span class="s2">&quot;all&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;min&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;max&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variance&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;variation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;standard_deviation&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;sum_squares_centered&quot;</span><span class="p">,</span>
+                        <span class="s2">&quot;second_order_raw_moment&quot;</span><span class="p">,</span>
+                    <span class="p">}</span>
+                <span class="p">),</span>
+                <span class="nb">list</span><span class="p">,</span>
+            <span class="p">],</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">result_options</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">result_options</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_basic_statistics</span><span class="o">.</span><span class="n">get_all_result_options</span><span class="p">()</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">result_options</span> <span class="o">=</span> <span class="n">result_options</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearn.basic_statistics.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="k">def</span> <span class="nf">_get_onedal_result_options</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">options</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">onedal_options</span> <span class="o">=</span> <span class="s2">&quot;|&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">result_options</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">onedal_options</span> <span class="o">=</span> <span class="n">options</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">onedal_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">options</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_finalize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+        <span class="n">first_pass</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;n_samples_seen_&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">==</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">check_input</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="p">,</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">reset</span><span class="o">=</span><span class="n">first_pass</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">_check_sample_weight</span><span class="p">(</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">first_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">+=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;result_options&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_onedal_result_options</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">result_options</span><span class="p">)</span>
+        <span class="p">}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_basic_statistics</span><span class="p">(</span>
+                <span class="o">**</span><span class="n">onedal_params</span>
+            <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">_check_sample_weight</span><span class="p">(</span><span class="n">sample_weight</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+
+        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">n_features</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">gen_batches</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span><span class="p">):</span>
+            <span class="n">X_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span>
+            <span class="n">weights_batch</span> <span class="o">=</span> <span class="n">sample_weight</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span> <span class="k">if</span> <span class="n">sample_weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">(</span>
+                <span class="n">X_batch</span><span class="p">,</span> <span class="n">weights_batch</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attr</span><span class="p">):</span>
+        <span class="n">result_options</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;result_options&quot;</span><span class="p">]</span>
+        <span class="n">sattr</span> <span class="o">=</span> <span class="n">attr</span><span class="o">.</span><span class="n">removesuffix</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)</span>
+        <span class="n">is_statistic_attr</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">sattr</span> <span class="o">==</span> <span class="n">result_options</span><span class="p">)</span>
+        <span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">result_options</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">sattr</span> <span class="ow">in</span> <span class="n">result_options</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">is_statistic_attr</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">sattr</span> <span class="o">==</span> <span class="n">attr</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                    <span class="s2">&quot;Result attributes without a trailing underscore were deprecated in version 2025.1 and will be removed in 2026.0&quot;</span>
+                <span class="p">)</span>
+            <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="p">,</span> <span class="n">sattr</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">attr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">attr</span><span class="p">]</span>
+
+        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;</span><span class="si">{</span><span class="n">attr</span><span class="si">}</span><span class="s2">&#39;&quot;</span>
+        <span class="p">)</span>
+
+<div class="viewcode-block" id="IncrementalBasicStatistics.partial_fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Incremental fit with X. All of X is processed as a single batch.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Data for compute, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Weights for compute weighted statistics, where ``n_samples`` is the number of samples.</span>
+
+<span class="sd">        check_input : bool, default=True</span>
+<span class="sd">            Run ``check_array`` on X.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="p">,</span>
+            <span class="n">check_input</span><span class="o">=</span><span class="n">check_input</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="IncrementalBasicStatistics.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calculate statistics of X using minibatches of size batch_size.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Data for compute, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Weights for compute weighted statistics, where ``n_samples`` is the number of samples.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f04b68e4350>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
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+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/latest/_modules/sklearnex/covariance/incremental_covariance.html b/latest/_modules/sklearnex/covariance/incremental_covariance.html
new file mode 100644
index 0000000000..88beb8a113
--- /dev/null
+++ b/latest/_modules/sklearnex/covariance/incremental_covariance.html
@@ -0,0 +1,886 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>sklearnex.covariance.incremental_covariance &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=e59714d7" />
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+      <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+      <script src="../../../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
+    <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
+        var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
+        var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
+        // Load TMS
+        if(document.location.href.includes("intel.github.io/scikit-learn-intelex")){
+          (function () {
+            var url = 'https://www.intel.com/content/dam/www/global/wap/tms-loader.js'; // WAP file URL
+            var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = url;
+            var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
+          })();
+      }
+      </script>
+
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../../../index.html" class="icon icon-home">
+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
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+      <li class="breadcrumb-item active">sklearnex.covariance.incremental_covariance</li>
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+      </li>
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+             
+  <h1>Source code for sklearnex.covariance.incremental_covariance</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ===============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ===============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">linalg</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">clone</span>
+<span class="kn">from</span> <span class="nn">sklearn.covariance</span> <span class="kn">import</span> <span class="n">EmpiricalCovariance</span> <span class="k">as</span> <span class="n">_sklearn_EmpiricalCovariance</span>
+<span class="kn">from</span> <span class="nn">sklearn.covariance</span> <span class="kn">import</span> <span class="n">log_likelihood</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span><span class="p">,</span> <span class="n">gen_batches</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">_num_features</span><span class="p">,</span> <span class="n">check_is_fitted</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">daal_check_version</span><span class="p">,</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.covariance</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IncrementalEmpiricalCovariance</span> <span class="k">as</span> <span class="n">onedal_IncrementalEmpiricalCovariance</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">config_context</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span><span class="p">,</span> <span class="n">wrap_output_data</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">PatchingConditionsChain</span><span class="p">,</span> <span class="n">register_hyperparameters</span>
+<span class="kn">from</span> <span class="nn">..metrics</span> <span class="kn">import</span> <span class="n">pairwise_distances</span>
+<span class="kn">from</span> <span class="nn">..utils._array_api</span> <span class="kn">import</span> <span class="n">get_namespace</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">Interval</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.6&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">validate_data</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">validate_data</span> <span class="o">=</span> <span class="n">BaseEstimator</span><span class="o">.</span><span class="n">_validate_data</span>
+
+
+<div class="viewcode-block" id="IncrementalEmpiricalCovariance">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance">[docs]</a>
+<span class="nd">@control_n_jobs</span><span class="p">(</span><span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span> <span class="s2">&quot;fit&quot;</span><span class="p">,</span> <span class="s2">&quot;_onedal_finalize_fit&quot;</span><span class="p">])</span>
+<span class="k">class</span> <span class="nc">IncrementalEmpiricalCovariance</span><span class="p">(</span><span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Maximum likelihood covariance estimator that allows for the estimation when the data are split into</span>
+<span class="sd">    batches. The user can use the ``partial_fit`` method to provide a single batch of data or use the ``fit`` method to provide</span>
+<span class="sd">    the entire dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    store_precision : bool, default=False</span>
+<span class="sd">        Specifies if the estimated precision is stored.</span>
+
+<span class="sd">    assume_centered : bool, default=False</span>
+<span class="sd">        If True, data are not centered before computation.</span>
+<span class="sd">        Useful when working with data whose mean is almost, but not exactly</span>
+<span class="sd">        zero.</span>
+<span class="sd">        If False (default), data are centered before computation.</span>
+
+<span class="sd">    batch_size : int, default=None</span>
+<span class="sd">        The number of samples to use for each batch. Only used when calling</span>
+<span class="sd">        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``</span>
+<span class="sd">        is inferred from the data and set to ``5 * n_features``, to provide a</span>
+<span class="sd">        balance between approximation accuracy and memory consumption.</span>
+
+<span class="sd">    copy : bool, default=True</span>
+<span class="sd">        If False, X will be overwritten. ``copy=False`` can be used to</span>
+<span class="sd">        save memory but is unsafe for general use.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    location_ : ndarray of shape (n_features,)</span>
+<span class="sd">        Estimated location, i.e. the estimated mean.</span>
+
+<span class="sd">    covariance_ : ndarray of shape (n_features, n_features)</span>
+<span class="sd">        Estimated covariance matrix</span>
+
+<span class="sd">    n_samples_seen_ : int</span>
+<span class="sd">        The number of samples processed by the estimator. Will be reset on</span>
+<span class="sd">        new calls to ``fit``, but increments across ``partial_fit`` calls.</span>
+
+<span class="sd">    batch_size_ : int</span>
+<span class="sd">        Inferred batch size from ``batch_size``.</span>
+
+<span class="sd">    n_features_in_ : int</span>
+<span class="sd">        Number of features seen during ``fit`` or ``partial_fit``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.covariance import IncrementalEmpiricalCovariance</span>
+<span class="sd">    &gt;&gt;&gt; inccov = IncrementalEmpiricalCovariance(batch_size=1)</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.partial_fit(X[:1])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.partial_fit(X[1:])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.covariance_</span>
+<span class="sd">    np.array([[1., 1.],[1., 1.]])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.location_</span>
+<span class="sd">    np.array([2., 3.])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; inccov.covariance_</span>
+<span class="sd">    np.array([[1., 1.],[1., 1.]])</span>
+<span class="sd">    &gt;&gt;&gt; inccov.location_</span>
+<span class="sd">    np.array([2., 3.])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_onedal_incremental_covariance</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_IncrementalEmpiricalCovariance</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;store_precision&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;assume_centered&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+            <span class="s2">&quot;copy&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="n">get_precision</span> <span class="o">=</span> <span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">get_precision</span>
+    <span class="n">error_norm</span> <span class="o">=</span> <span class="n">wrap_output_data</span><span class="p">(</span><span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">error_norm</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">store_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">assume_centered</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">assume_centered</span> <span class="o">=</span> <span class="n">assume_centered</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">store_precision</span> <span class="o">=</span> <span class="n">store_precision</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">copy</span> <span class="o">=</span> <span class="n">copy</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearn.covariance.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_finalize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">daal_check_version</span><span class="p">((</span><span class="mi">2024</span><span class="p">,</span> <span class="s2">&quot;P&quot;</span><span class="p">,</span> <span class="mi">400</span><span class="p">))</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">assume_centered</span><span class="p">:</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">location_</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">covariance_</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">location</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">location</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">location_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">location</span><span class="p">))</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_precision</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">precision_</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">pinvh</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">covariance_</span><span class="p">,</span> <span class="n">check_finite</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">precision_</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">covariance_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">covariance_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;covariance_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">location_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">location_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;location_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+
+        <span class="n">first_pass</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;n_samples_seen_&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">==</span> <span class="mi">0</span>
+
+        <span class="c1"># finite check occurs on onedal side</span>
+        <span class="k">if</span> <span class="n">check_input</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="p">,</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">reset</span><span class="o">=</span><span class="n">first_pass</span><span class="p">,</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;method&quot;</span><span class="p">:</span> <span class="s2">&quot;dense&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;bias&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+            <span class="s2">&quot;assume_centered&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">assume_centered</span><span class="p">,</span>
+        <span class="p">}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_covariance</span><span class="p">(</span><span class="o">**</span><span class="n">onedal_params</span><span class="p">)</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">first_pass</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">+=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="k">finally</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="nd">@wrap_output_data</span>
+    <span class="k">def</span> <span class="nf">score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">xp</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">get_namespace</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+
+        <span class="n">check_is_fitted</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">location_</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">X_test</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X_test</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;numpy&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">xp</span><span class="o">.</span><span class="vm">__name__</span><span class="p">:</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="n">xp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X_test</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+            <span class="c1"># depending on the sklearn version, check_array</span>
+            <span class="c1"># and validate_data will return only numpy arrays</span>
+            <span class="c1"># which will break dpnp/dpctl support. If the</span>
+            <span class="c1"># array namespace isn&#39;t from numpy and the data</span>
+            <span class="c1"># is now a numpy array, it has been validated and</span>
+            <span class="c1"># the original can be used.</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">X_test</span>
+
+        <span class="n">est</span> <span class="o">=</span> <span class="n">clone</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">est</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s2">&quot;assume_centered&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span>
+
+        <span class="c1"># test_cov is a numpy array, but calculated on device</span>
+        <span class="n">test_cov</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span> <span class="o">-</span> <span class="n">location</span><span class="p">)</span><span class="o">.</span><span class="n">covariance_</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">log_likelihood</span><span class="p">(</span><span class="n">test_cov</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_precision</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="n">res</span>
+
+<div class="viewcode-block" id="IncrementalEmpiricalCovariance.partial_fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Incremental fit with X. All of X is processed as a single batch.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where `n_samples` is the number of samples and</span>
+<span class="sd">            `n_features` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        check_input : bool, default=True</span>
+<span class="sd">            Run check_array on X.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">check_input</span><span class="o">=</span><span class="n">check_input</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="IncrementalEmpiricalCovariance.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fit the model with X, using minibatches of size batch_size.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where `n_samples` is the number of samples and</span>
+<span class="sd">            `n_features` is the number of features.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="c1"># finite check occurs on onedal side</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span>
+                <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span> <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">,</span> <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="k">else</span> <span class="mi">5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span>
+
+        <span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Only one sample available. You may want to reshape your data array&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">gen_batches</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span><span class="p">):</span>
+            <span class="n">X_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">(</span><span class="n">X_batch</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="c1"># expose sklearnex pairwise_distances if mahalanobis distance eventually supported</span>
+    <span class="k">def</span> <span class="nf">mahalanobis</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_check_feature_names</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">xp</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">get_namespace</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">precision</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_precision</span><span class="p">()</span>
+        <span class="c1"># compute mahalanobis distances</span>
+        <span class="c1"># pairwise_distances will check n_features (via n_feature matching with</span>
+        <span class="c1"># self.location_) , and will check for finiteness via check array</span>
+        <span class="c1"># check_feature_names will match _validate_data functionally</span>
+        <span class="n">location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">location_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span>
+        <span class="k">if</span> <span class="s2">&quot;numpy&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">xp</span><span class="o">.</span><span class="vm">__name__</span><span class="p">:</span>
+            <span class="c1"># Guarantee that inputs to pairwise_distances match in type and location</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="n">xp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">pairwise_distances</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">location</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;mahalanobis&quot;</span><span class="p">,</span> <span class="n">VI</span><span class="o">=</span><span class="n">precision</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="c1"># Throw the expected sklearn error in an n_feature length violation</span>
+            <span class="k">if</span> <span class="s2">&quot;Incompatible dimension for X and Y matrices: X.shape[1] ==&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;X has </span><span class="si">{</span><span class="n">_num_features</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="si">}</span><span class="s2"> features, but </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;is expecting </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span><span class="si">}</span><span class="s2"> features as input.&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="n">e</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">xp</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dist</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,)))</span> <span class="o">**</span> <span class="mi">2</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="n">mahalanobis</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">mahalanobis</span><span class="o">.</span><span class="vm">__doc__</span>
+    <span class="n">error_norm</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">error_norm</span><span class="o">.</span><span class="vm">__doc__</span>
+    <span class="n">score</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_sklearn_EmpiricalCovariance</span><span class="o">.</span><span class="n">score</span><span class="o">.</span><span class="vm">__doc__</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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diff --git a/latest/_modules/sklearnex/linear_model/incremental_linear.html b/latest/_modules/sklearnex/linear_model/incremental_linear.html
new file mode 100644
index 0000000000..ee286a3205
--- /dev/null
+++ b/latest/_modules/sklearnex/linear_model/incremental_linear.html
@@ -0,0 +1,973 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>sklearnex.linear_model.incremental_linear &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
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+    <link rel="index" title="Index" href="../../../genindex.html" />
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
+        // Configure TMS settings
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+      </script>
+
+</head>
+
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+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+        <div class="wy-side-nav-search" >
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+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+  <div class="version-switcher__container dropdown">
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+      current_version = "2025.1"
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../kaggle.html#acceleration">Acceleration</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#binary-classification">Binary Classification</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../../../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../kaggle/automl.html">AutoML Workflows</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id6">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../algorithms.html#id12">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+      </li>
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+             
+  <h1>Source code for sklearnex.linear_model.incremental_linear</h1><div class="highlight"><pre>
+<span></span><span class="c1"># ===============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ===============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">MultiOutputMixin</span><span class="p">,</span> <span class="n">RegressorMixin</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">r2_score</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span><span class="p">,</span> <span class="n">gen_batches</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">check_is_fitted</span>
+
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._n_jobs_support</span> <span class="kn">import</span> <span class="n">control_n_jobs</span>
+<span class="kn">from</span> <span class="nn">daal4py.sklearn._utils</span> <span class="kn">import</span> <span class="n">sklearn_check_version</span>
+<span class="kn">from</span> <span class="nn">onedal.linear_model</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IncrementalLinearRegression</span> <span class="k">as</span> <span class="n">onedal_IncrementalLinearRegression</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils._param_validation</span> <span class="kn">import</span> <span class="n">Interval</span>
+
+<span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.6&quot;</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">validate_data</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">validate_data</span> <span class="o">=</span> <span class="n">BaseEstimator</span><span class="o">.</span><span class="n">_validate_data</span>
+
+<span class="kn">from</span> <span class="nn">onedal.common.hyperparameters</span> <span class="kn">import</span> <span class="n">get_hyperparameters</span>
+
+<span class="kn">from</span> <span class="nn">.._device_offload</span> <span class="kn">import</span> <span class="n">dispatch</span><span class="p">,</span> <span class="n">wrap_output_data</span>
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">PatchingConditionsChain</span><span class="p">,</span> <span class="n">register_hyperparameters</span>
+
+
+<div class="viewcode-block" id="IncrementalLinearRegression">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression">[docs]</a>
+<span class="nd">@register_hyperparameters</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;fit&quot;</span><span class="p">:</span> <span class="n">get_hyperparameters</span><span class="p">(</span><span class="s2">&quot;linear_regression&quot;</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">),</span>
+        <span class="s2">&quot;partial_fit&quot;</span><span class="p">:</span> <span class="n">get_hyperparameters</span><span class="p">(</span><span class="s2">&quot;linear_regression&quot;</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">),</span>
+    <span class="p">}</span>
+<span class="p">)</span>
+<span class="nd">@control_n_jobs</span><span class="p">(</span>
+    <span class="n">decorated_methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;fit&quot;</span><span class="p">,</span> <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span> <span class="s2">&quot;predict&quot;</span><span class="p">,</span> <span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="s2">&quot;_onedal_finalize_fit&quot;</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="k">class</span> <span class="nc">IncrementalLinearRegression</span><span class="p">(</span>
+    <span class="n">IntelEstimator</span><span class="p">,</span> <span class="n">MultiOutputMixin</span><span class="p">,</span> <span class="n">RegressorMixin</span><span class="p">,</span> <span class="n">BaseEstimator</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Trains a linear regression model, allows for computation if the data are split into</span>
+<span class="sd">    batches. The user can use the ``partial_fit`` method to provide a single batch of data or use the ``fit`` method to provide</span>
+<span class="sd">    the entire dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    fit_intercept : bool, default=True</span>
+<span class="sd">    Whether to calculate the intercept for this model. If set</span>
+<span class="sd">    to False, no intercept will be used in calculations</span>
+<span class="sd">    (i.e. data is expected to be centered).</span>
+
+<span class="sd">    copy_X : bool, default=True</span>
+<span class="sd">        If True, X will be copied; else, it may be overwritten.</span>
+
+<span class="sd">    n_jobs : int, default=None</span>
+<span class="sd">        The number of jobs to use for the computation.</span>
+
+<span class="sd">    batch_size : int, default=None</span>
+<span class="sd">        The number of samples to use for each batch. Only used when calling</span>
+<span class="sd">        ``fit``. If ``batch_size`` is ``None``, then ``batch_size``</span>
+<span class="sd">        is inferred from the data and set to ``5 * n_features``.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    coef_ : array of shape (n_features, ) or (n_targets, n_features)</span>
+<span class="sd">        Estimated coefficients for the linear regression problem.</span>
+<span class="sd">        If multiple targets are passed during the fit (y 2D), this</span>
+<span class="sd">        is a 2D array of shape (n_targets, n_features), while if only</span>
+<span class="sd">        one target is passed, this is a 1D array of length n_features.</span>
+
+<span class="sd">    intercept_ : float or array of shape (n_targets,)</span>
+<span class="sd">        Independent term in the linear model. Set to 0.0 if</span>
+<span class="sd">        `fit_intercept = False`.</span>
+
+<span class="sd">    n_samples_seen_ : int</span>
+<span class="sd">        The number of samples processed by the estimator. Will be reset on</span>
+<span class="sd">        new calls to ``fit``, but increments across ``partial_fit`` calls.</span>
+<span class="sd">        It should be not less than `n_features_in_` if `fit_intercept`</span>
+<span class="sd">        is False and not less than `n_features_in_` + 1 if `fit_intercept`</span>
+<span class="sd">        is True to obtain regression coefficients.</span>
+
+<span class="sd">    batch_size_ : int</span>
+<span class="sd">        Inferred batch size from ``batch_size``.</span>
+
+<span class="sd">    n_features_in_ : int</span>
+<span class="sd">        Number of features seen during ``fit`` or ``partial_fit``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from sklearnex.linear_model import IncrementalLinearRegression</span>
+<span class="sd">    &gt;&gt;&gt; inclr = IncrementalLinearRegression(batch_size=2)</span>
+<span class="sd">    &gt;&gt;&gt; X = np.array([[1, 2], [3, 4], [5, 6], [7, 10]])</span>
+<span class="sd">    &gt;&gt;&gt; y = np.array([1.5, 3.5, 5.5, 8.5])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.partial_fit(X[:2], y[:2])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.partial_fit(X[2:], y[2:])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.coef_</span>
+<span class="sd">    np.array([0.5., 0.5.])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.intercept_</span>
+<span class="sd">    np.array(0.)</span>
+<span class="sd">    &gt;&gt;&gt; inclr.fit(X)</span>
+<span class="sd">    &gt;&gt;&gt; inclr.coef_</span>
+<span class="sd">    np.array([0.5., 0.5.])</span>
+<span class="sd">    &gt;&gt;&gt; inclr.intercept_</span>
+<span class="sd">    np.array(0.)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_onedal_incremental_linear</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">onedal_IncrementalLinearRegression</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+        <span class="n">_parameter_constraints</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;boolean&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;n_jobs&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">Interval</span><span class="p">(</span><span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">),</span> <span class="kc">None</span><span class="p">],</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">fit_intercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">copy_X</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span> <span class="o">=</span> <span class="n">fit_intercept</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span> <span class="o">=</span> <span class="n">copy_X</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_supported</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="o">*</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">patching_status</span> <span class="o">=</span> <span class="n">PatchingConditionsChain</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;sklearn.linear_model.</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">method_name</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">patching_status</span>
+
+    <span class="n">_onedal_cpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+    <span class="n">_onedal_gpu_supported</span> <span class="o">=</span> <span class="n">_onedal_supported</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">r2_score</span><span class="p">(</span>
+            <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_predict</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">),</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">first_pass</span> <span class="o">=</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;n_samples_seen_&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">==</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">check_input</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+                <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="p">,</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">y</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">reset</span><span class="o">=</span><span class="n">first_pass</span><span class="p">,</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                    <span class="n">multi_output</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">X</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                    <span class="n">y</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                    <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">force_all_finite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">first_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">+=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">onedal_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;fit_intercept&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span><span class="p">,</span> <span class="s2">&quot;copy_X&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_incremental_linear</span><span class="p">(</span><span class="o">**</span><span class="n">onedal_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_finalize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">)</span>
+        <span class="n">is_underdetermined</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="n">is_underdetermined</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Not enough samples to finalize&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">_onedal_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.0&quot;</span><span class="p">):</span>
+            <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">validate_data</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">y</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+                <span class="n">multi_output</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy_X</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span>
+                <span class="n">y</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">],</span>
+                <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">ensure_2d</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="n">is_underdetermined</span> <span class="o">=</span> <span class="n">n_samples</span> <span class="o">&lt;</span> <span class="n">n_features</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">is_underdetermined</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Not enough samples to run oneDAL backend&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">n_features</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_samples_seen_</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">gen_batches</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size_</span><span class="p">):</span>
+            <span class="n">X_batch</span><span class="p">,</span> <span class="n">y_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">batch</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">(</span><span class="n">X_batch</span><span class="p">,</span> <span class="n">y_batch</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sklearn_check_version</span><span class="p">(</span><span class="s2">&quot;1.2&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
+
+        <span class="c1"># finite check occurs on onedal side</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">n_features</span>
+
+        <span class="k">if</span> <span class="n">n_samples</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Only one sample available. You may want to reshape your data array&quot;</span>
+            <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">(</span><span class="n">queue</span><span class="o">=</span><span class="n">queue</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">intercept_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">intercept_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;intercept_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="nd">@intercept_</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">intercept_</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;intercept_&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">intercept_</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_onedal_model</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">coef_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_need_to_finalize</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_finalize_fit</span><span class="p">()</span>
+
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">coef_</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; object has no attribute &#39;coef_&#39;&quot;</span>
+            <span class="p">)</span>
+
+    <span class="nd">@coef_</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">coef_</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;coef_&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_onedal_estimator&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">coef_</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_onedal_estimator</span><span class="o">.</span><span class="n">_onedal_model</span>
+
+<div class="viewcode-block" id="IncrementalLinearRegression.partial_fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">check_input</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Incremental fit linear model with X and y. All of X and y is</span>
+<span class="sd">        processed as a single batch.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            `n_features` is the number of features.</span>
+
+<span class="sd">        y : array-like of shape (n_samples,) or (n_samples, n_targets)</span>
+<span class="sd">            Target values, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_targets`` is the number of targets.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;partial_fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_partial_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">y</span><span class="p">,</span>
+            <span class="n">check_input</span><span class="o">=</span><span class="n">check_input</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="IncrementalLinearRegression.fit">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fit the model with X and y, using minibatches of size ``batch_size``.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Training data, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_features`` is the number of features. It is necessary for</span>
+<span class="sd">            ``n_samples`` to be not less than ``n_features`` if ``fit_intercept``</span>
+<span class="sd">            is False and not less than ``n_features + 1`` if ``fit_intercept``</span>
+<span class="sd">            is True</span>
+
+<span class="sd">        y : array-like of shape (n_samples,) or (n_samples, n_targets)</span>
+<span class="sd">            Target values, where ``n_samples`` is the number of samples and</span>
+<span class="sd">            ``n_targets`` is the number of targets.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : object</span>
+<span class="sd">            Returns the instance itself.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;fit&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_fit</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">y</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="IncrementalLinearRegression.predict">
+<a class="viewcode-back" href="../../../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict">[docs]</a>
+    <span class="nd">@wrap_output_data</span>
+    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Predict using the linear model.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like or sparse matrix, shape (n_samples, n_features)</span>
+<span class="sd">            Samples.</span>
+
+<span class="sd">        y : Ignored</span>
+<span class="sd">            Not used, present for API consistency by convention.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        C : array, shape (n_samples, n_targets)</span>
+<span class="sd">            Returns predicted values.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">check_is_fitted</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;predict&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_predict</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="nd">@wrap_output_data</span>
+    <span class="k">def</span> <span class="nf">score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the coefficient of determination of the prediction.</span>
+
+<span class="sd">        The coefficient of determination :math:`R^2` is defined as</span>
+<span class="sd">        :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual</span>
+<span class="sd">        sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`</span>
+<span class="sd">        is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.</span>
+<span class="sd">        The best possible score is 1.0 and it can be negative (because the</span>
+<span class="sd">        model can be arbitrarily worse). A constant model that always predicts</span>
+<span class="sd">        the expected value of `y`, disregarding the input features, would get</span>
+<span class="sd">        a :math:`R^2` score of 0.0.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X : array-like of shape (n_samples, n_features)</span>
+<span class="sd">            Test samples. For some estimators this may be a precomputed</span>
+<span class="sd">            kernel matrix or a list of generic objects instead with shape</span>
+<span class="sd">            ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``</span>
+<span class="sd">            is the number of samples used in the fitting for the estimator.</span>
+
+<span class="sd">        y : array-like of shape (n_samples,) or (n_samples, n_outputs)</span>
+<span class="sd">            True values for `X`.</span>
+
+<span class="sd">        sample_weight : array-like of shape (n_samples,), default=None</span>
+<span class="sd">            Sample weights.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        score : float</span>
+<span class="sd">            :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        The :math:`R^2` score used when calling ``score`` on a regressor uses</span>
+<span class="sd">        ``multioutput=&#39;uniform_average&#39;`` from version 0.23 to keep consistent</span>
+<span class="sd">        with default value of :func:`~sklearn.metrics.r2_score`.</span>
+<span class="sd">        This influences the ``score`` method of all the multioutput</span>
+<span class="sd">        regressors (except for</span>
+<span class="sd">        :class:`~sklearn.multioutput.MultiOutputRegressor`).</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">check_is_fitted</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">dispatch</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="s2">&quot;score&quot;</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;onedal&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">_onedal_score</span><span class="p">,</span>
+                <span class="s2">&quot;sklearn&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="n">X</span><span class="p">,</span>
+            <span class="n">y</span><span class="p">,</span>
+            <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f04b63847d0>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
+    Cookies
+</a>
+<a href=
+    "https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html">
+    Privacy
+</a>
+<a data-wap_ref="dns" id="wap_dns" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html">
+    Do Not Share My Personal Information
+</a>
+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/latest/_sources/algorithms.rst.txt b/latest/_sources/algorithms.rst.txt
index 73397f31c3..4d165c1910 100644
--- a/latest/_sources/algorithms.rst.txt
+++ b/latest/_sources/algorithms.rst.txt
@@ -46,7 +46,7 @@ Classification
      - All parameters are supported except:
 
        - ``warm_start`` = `True`
-       - ``cpp_alpha`` != `0`
+       - ``ccp_alpha`` != `0`
        - ``criterion`` != `'gini'`
      - Multi-output and sparse data are not supported
    * - `KNeighborsClassifier`
@@ -87,7 +87,7 @@ Regression
      - All parameters are supported except:
 
        - ``warm_start`` = `True`
-       - ``cpp_alpha`` != `0`
+       - ``ccp_alpha`` != `0`
        - ``criterion`` != `'mse'`
      - Multi-output and sparse data are not supported
    * - `KNeighborsRegressor`
@@ -100,7 +100,7 @@ Regression
 
        - ``normalize`` != `False`
        - ``sample_weight`` != `None`
-     - Only dense data is supported, `#observations` should be >= `#features`.
+     - Only dense data is supported.
    * - `Ridge`
      - All parameters are supported except:
 
@@ -143,7 +143,7 @@ Clustering
        - ``algorithm`` not in [`'brute'`, `'auto'`]
      - Only dense data is supported
 
-Dimensionality reduction
+Dimensionality Reduction
 ************************
 
 .. list-table::
@@ -157,7 +157,10 @@ Dimensionality reduction
    * - `PCA`
      - All parameters are supported except:
 
-       - ``svd_solver`` != `'full'`
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+     - Sparse data is not supported
+   * - `IncrementalPCA`
+     - All parameters are supported
      - Sparse data is not supported
    * - `TSNE`
      - All parameters are supported except:
@@ -188,7 +191,7 @@ Nearest Neighbors
          all parameters except ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
      - Sparse data is not supported
 
-Other tasks
+Other Tasks
 ***********
 
 .. list-table::
@@ -199,6 +202,9 @@ Other tasks
    * - Algorithm
      - Parameters
      - Data formats
+   * - `EmpiricalCovariance`
+     - All parameters are supported
+     - Only dense data is supported
    * - `train_test_split`
      - All parameters are supported
      - Only dense data is supported
@@ -245,7 +251,7 @@ Classification
      - All parameters are supported except:
 
        - ``warm_start`` = `True`
-       - ``cpp_alpha`` != `0`
+       - ``ccp_alpha`` != `0`
        - ``criterion`` != `'gini'`
        - ``oob_score`` = `True`
        - ``sample_weight`` != `None`
@@ -281,7 +287,7 @@ Regression
      - All parameters are supported except:
 
        - ``warm_start`` = `True`
-       - ``cpp_alpha`` != `0`
+       - ``ccp_alpha`` != `0`
        - ``criterion`` != `'mse'`
        - ``oob_score`` = `True`
        - ``sample_weight`` != `None`
@@ -298,7 +304,7 @@ Regression
 
        - ``normalize`` != `False`
        - ``sample_weight`` != `None`
-     - Only dense data is supported, `#observations` should be >= `#features`.
+     - Only dense data is supported.
 
 Clustering
 **********
@@ -316,8 +322,7 @@ Clustering
 
        - ``precompute_distances``
        - ``sample_weight`` != `None`
-       
-       ``Init`` = `'k-means++'` fallbacks to CPU.
+       - ``Init`` = `'k-means++'` fallbacks to CPU.
      - Sparse data is not supported
    * - `DBSCAN`
      - All parameters are supported except:
@@ -326,7 +331,7 @@ Clustering
        - ``algorithm`` not in [`'brute'`, `'auto'`]
      - Only dense data is supported
 
-Dimensionality reduction
+Dimensionality Reduction
 ************************
 
 .. list-table::
@@ -340,7 +345,7 @@ Dimensionality reduction
    * - `PCA`
      - All parameters are supported except:
      
-       - ``svd_solver`` != `'full'`
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
      - Sparse data is not supported
 
 Nearest Neighbors
@@ -362,12 +367,177 @@ Nearest Neighbors
        - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
      - Only dense data is supported
 
-Scikit-learn tests
+Other Tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `EmpiricalCovariance`
+     - All parameters are supported
+     - Only dense data is supported
+
+SPMD Support
+------------
+
+.. seealso:: :ref:`distributed`
+
+Classification
+**************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `RandomForestClassifier`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'gini'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsClassifier`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+       - ``predict_proba`` method not supported
+     - Only dense data is supported
+   * - `LogisticRegression`
+     - All parameters are supported except:
+
+       - ``solver`` != `'newton-cg'`
+       - ``class_weight`` != `None`
+       - ``sample_weight`` != `None`
+       - ``penalty`` != `'l2'`
+     - Only dense data is supported
+
+Regression
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `RandomForestRegressor`
+     - All parameters are supported except:
+
+       - ``warm_start`` = `True`
+       - ``ccp_alpha`` != `0`
+       - ``criterion`` != `'mse'`
+       - ``oob_score`` = `True`
+       - ``sample_weight`` != `None`
+     - Multi-output and sparse data are not supported
+   * - `KNeighborsRegressor`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` != `'euclidean'` or `'minkowski'` with ``p`` != `2`
+     - Only dense data is supported
+   * - `LinearRegression`
+     - All parameters are supported except:
+
+       - ``normalize`` != `False`
+       - ``sample_weight`` != `None`
+     - Only dense data is supported.
+
+Clustering
+**********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `KMeans`
+     - All parameters are supported except:
+
+       - ``precompute_distances``
+       - ``sample_weight`` != `None`
+       - ``Init`` = `'k-means++'` fallbacks to CPU.
+     - Sparse data is not supported
+   * - `DBSCAN`
+     - All parameters are supported except:
+
+       - ``metric`` != `'euclidean'`
+       - ``algorithm`` not in [`'brute'`, `'auto'`]
+     - Only dense data is supported
+
+Dimensionality Reduction
+************************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters & Methods
+     - Data formats
+   * - `PCA`
+     - All parameters are supported except:
+     
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
+       - ``fit`` is the only method supported
+     - Sparse data is not supported
+
+Nearest Neighbors
+*****************
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `NearestNeighbors`
+     - All parameters are supported except:
+
+       - ``algorithm`` != `'brute'`
+       - ``weights`` = `'callable'`
+       - ``metric`` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]
+     - Only dense data is supported
+
+Other Tasks
+***********
+
+.. list-table::
+   :widths: 10 30 20
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Parameters
+     - Data formats
+   * - `EmpiricalCovariance`
+     - All parameters are supported
+     - Only dense data is supported
+
+Scikit-learn Tests
 ------------------
 
 Monkey-patched scikit-learn classes and functions passes scikit-learn's own test
 suite, with few exceptions, specified in `deselected_tests.yaml
-<https://github.com/intel/scikit-learn-intelex/blob/master/deselected_tests.yaml>`__.
-
-The results of the entire latest scikit-learn test suite with |intelex|: `CircleCI
-<https://circleci.com/gh/intel/scikit-learn-intelex>`_.
\ No newline at end of file
+<https://github.com/intel/scikit-learn-intelex/blob/main/deselected_tests.yaml>`__.
diff --git a/latest/_sources/array_api.rst.txt b/latest/_sources/array_api.rst.txt
new file mode 100644
index 0000000000..4c71e0cb88
--- /dev/null
+++ b/latest/_sources/array_api.rst.txt
@@ -0,0 +1,123 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _array_api:
+
+=================
+Array API support
+=================
+The `Array API <https://data-apis.org/array-api/latest/>`_ specification defines
+a standard API for all array manipulation libraries with a NumPy-like API.
+Intel(R) Extension for Scikit-Learn doesn't require
+`array-api-compat <https://github.com/data-apis/array-api-compat>`__ to be installed for
+functional support of the array API standard.
+In the current implementation, the functional support of array api follows the functional
+support of different array or DataFrame inputs and does not modify the precision of the
+input and output data formats unless necessary. Any array API input will be converted to host
+numpy.ndarrays and all internal manipulations with data will be done with these representations of
+the input data. DPNP's 'ndarray' and Data Parallel Control's 'usm_ndarray' have special handling
+requirements that are described in the relevant section of this document. Output values will in
+all relevant cases match the input data format.
+
+.. note::
+    Currently, only `array-api-strict <https://github.com/data-apis/array-api-strict>`__,
+    `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__, `dpnp <https://github.com/IntelPython/dpnp>`__
+    and `numpy <https://numpy.org/>`__ are known to work with sklearnex estimators.
+.. note::
+    Stock Scikit-learn’s array API support requires `array-api-compat <https://github.com/data-apis/array-api-compat>`__ to be installed.
+
+
+Support for DPNP and DPCTL
+==========================
+The functional support of input data for sklearnex estimators also extended for SYCL USM array types.
+These include SYCL USM arrays `dpnp's <https://github.com/IntelPython/dpnp>`__ ndarray and
+`Data Parallel Control usm_ndarray <https://intelpython.github.io/dpctl/latest/index.html>`__.
+DPNP ndarray and Data Parallel Control usm_ndarray contain SYCL contexts which can be used for
+`sklearnex` device offloading.
+
+.. note::
+    Current support for DPNP and DPCTL usm_ndarray data can be copied and moved to and from device in sklearnex and have
+    impacts on memory utilization.
+
+DPCTL or DPNP inputs are not required to use `config_context(target_offload=device)`.
+`sklearnex` will use input usm_ndarray sycl context for device offloading.
+
+.. note::
+    As DPCTL or DPNP inputs contain SYCL contexts, they do not require `config_context(target_offload=device)`.
+    However, the use of `config_context`` will override the contained SYCL context and will force movement
+    of data to the targeted device.
+
+
+Support for Array API-compatible inputs
+=======================================
+All patched estimators, metrics, tools and non-scikit-learn estimators functionally support Array API standard.
+Intel(R) Extension for scikit-Learn preserves input data format for all outputs. For all array inputs except
+SYCL USM arrays `dpnp's <https://github.com/IntelPython/dpnp>`__ ndarray and
+`Data Parallel Control usm_ndarray <https://intelpython.github.io/dpctl/latest/index.html>`__ all computation
+will be only accomplished on CPU unless specified by a `config_context`` with an available GPU device.
+
+Stock scikit-learn uses `config_context(array_api_dispatch=True)` for enabling Array API
+`support <https://scikit-learn.org/1.5/modules/array_api.html>`__.
+If `array_api_dispatch` is enabled and the installed Scikit-Learn version supports array API, then the original
+inputs are used when falling back to Scikit-Learn functionality.
+
+.. note::
+    Data Parallel Control usm_ndarray or DPNP ndarray inputs will use host numpy data copies when
+    falling back to Scikit-Learn since they are not array API compliant.
+.. note::
+    Functional support doesn't guarantee that after the model is trained, fitted attributes that are arrays
+    will also be from the same namespace as the training data.
+
+
+Example usage
+=============
+
+DPNP ndarrays
+-------------
+
+Here is an example code to demonstrate how to use `dpnp <https://github.com/IntelPython/dpnp>`__ arrays to
+run `RandomForestRegressor` on a GPU without `config_context(array_api_dispatch=True)`:
+
+.. literalinclude:: ../../examples/sklearnex/random_forest_regressor_dpnp.py
+	   :language: python
+
+
+.. note::
+    Functional support doesn't guarantee that after the model is trained, fitted attributes that are arrays
+    will also be from the same namespace as the training data.
+
+For example, if `dpnp's <https://github.com/IntelPython/dpnp>`__ namespace was used for training,
+then fitted attributes will be on the CPU and `numpy.ndarray` data format.
+
+DPCTL usm_ndarrays
+------------------
+Here is an example code to demonstrate how to use `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__
+arrays to run `RandomForestClassifier` on a GPU witout `config_context(array_api_dispatch=True)`:
+
+.. literalinclude:: ../../examples/sklearnex/random_forest_classifier_dpctl.py
+	   :language: python
+
+As on previous example, if `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__ Array API namespace was
+used for training, then fitted attributes will be on the CPU and `numpy.ndarray` data format.
+
+Use of `array-api-strict`
+-------------------------
+
+Here is an example code to demonstrate how to use `array-api-strict <https://github.com/data-apis/array-api-strict>`__
+arrays to run `DBSCAN`.
+
+.. literalinclude:: ../../examples/sklearnex/dbscan_array_api.py
+	   :language: python
diff --git a/latest/_sources/contribute.rst.txt b/latest/_sources/contribute.rst.txt
index b72c515b06..28283bd9d8 100644
--- a/latest/_sources/contribute.rst.txt
+++ b/latest/_sources/contribute.rst.txt
@@ -14,40 +14,51 @@
 .. * limitations under the License.
 .. *******************************************************************************/
 
+##################
 How to Contribute
------------------
+##################
 
-We welcome community contributions to |intelex|. You can:
+As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn. 
+This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.
 
-- Submit your changes directly with a `pull request <https://github.com/intel/scikit-learn-intelex/pulls>`_.
-- Log a bug or make a feature request with an `issue <https://github.com/intel/scikit-learn-intelex/issues>`_.
+Licensing
+----------------------------------
+Intel(R) Extension for Scikit-learn uses the `Apache 2.0 License <https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE>`_. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.
 
-Refer to our guidelines on pull requests and issues before you proceed.
+Pull Requests
+----------------------------------
+No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.
 
-Issues
-******
+Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:
 
-Use `GitHub issues <https://github.com/intel/scikit-learn-intelex/issues>`_ to:
+::
 
-- report an issue
-- make a feature request
+  git config --global user.name "Kate Developer"
+  git config --global user.email kate.developer@company.com
 
-.. note::
-   To report a vulnerability, refer to
-   `Intel vulnerability reporting policy <https://www.intel.com/content/www/us/en/security-center/default.html>`_.
+Before Contributing Changes
+********************************************
 
-Pull Requests
-*************
+Make sure you can build the product and run all the tests with your patch.
+For a larger feature, provide a relevant test.
+Document your code. Our project uses reStructuredText for documentation.
+For new file(s), specify the appropriate copyright year in the first line.
+Submit a pull request into the master branch.
+Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.
+
+Code Style
+-------------------
+
+We use `black <https://black.readthedocs.io/en/stable/>`_ and `isort <https://pycqa.github.io/isort/>`_ formatters for Python* code. 
+The line length is 90 characters; use default options otherwise. You can find the linter configuration in `.pyproject.toml <https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml>`_.
+
+A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.
+
+Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:
+
+::
 
-To contribute your changes directly to the repository, do the following:
+  pip install pre-commit
+  pre-commit install
 
-- Make sure you can build the product and run all the examples with your patch.
-- For a larger feature, provide a relevant example.
-- Document your code.
-- `Submit <https://github.com/intel/scikit-learn-intelex/pulls>`_ a pull request into the `master` branch.
-  Provide a brief description of the changes you are contributing.
 
-Public CI is enabled for the repository. Your PR should pass all of our checks.
-We will review your contribution and, if any additional fixes or modifications are necessary,
-we may give some feedback to guide you.
-When accepted, your pull request will be merged into our GitHub repository.
diff --git a/latest/_sources/distributed-mode.rst.txt b/latest/_sources/distributed-mode.rst.txt
index 79dd663a3a..cfeee2c611 100644
--- a/latest/_sources/distributed-mode.rst.txt
+++ b/latest/_sources/distributed-mode.rst.txt
@@ -19,12 +19,34 @@
 Distributed Mode
 ================
 
-.. note::
+|intelex| offers Single Program, Multiple Data (SPMD) supported interfaces for distributed computing.
+Several `GPU-supported algorithms <https://intel.github.io/scikit-learn-intelex/latest/oneapi-gpu.html#>`_ 
+also provide distributed, multi-GPU computing capabilities via integration with ``mpi4py``. The prerequisites 
+match those of GPU computing, along with an MPI backend of your choice (`Intel MPI recommended 
+<https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html#gs.dcan6r>`_, available 
+via ``impi-devel`` python package) and the ``mpi4py`` python package. If using |intelex| 
+`installed from sources <https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md#build-from-sources>`_,
+ensure that the spmd_backend is built.
 
-   |intelex| contains scikit-learn patching functionality that was originally available in 
-   `daal4py <https://github.com/intel/scikit-learn-intelex/tree/master/daal4py>`_ package.
-   We recommend you to use scikit-learn-intelex package instead of daal4py.
-   You can learn more about daal4py in `daal4py documentation <https://intelpython.github.io/daal4py>`_.
+Note that |intelex| now supports GPU offloading to speed up MPI operations. This is supported automatically with 
+some MPI backends, but in order to use GPU offloading with Intel MPI, set the following environment variable (providing
+data on device without this may lead to a runtime error):
+ 
+::
 
-While daal4py is available in `distribued mode <https://intelpython.github.io/daal4py/scaling.html>`_,
-|intelex| does not currently offer this functionality.
+     export I_MPI_OFFLOAD=1
+
+Estimators can be imported from the ``sklearnex.spmd`` module. Data should be distributed across multiple nodes as 
+desired, and should be transfered to a dpctl or dpnp array before being passed to the estimator. View a full 
+example of this process in the |intelex| repository, where many examples of our SPMD-supported estimators are 
+available: https://github.com/intel/scikit-learn-intelex/blob/main/examples/sklearnex/. To run:
+
+::
+
+  mpirun -n 4 python linear_regression_spmd.py
+
+Note that additional mpirun arguments can be added as desired. SPMD-supported estimators are listed in the 
+`algorithms support documentation <https://intel.github.io/scikit-learn-intelex/latest/algorithms.html#spmd-support>`_.
+
+Additionally, daal4py offers some distributed functionality, see 
+`documentation <https://intelpython.github.io/daal4py/scaling.html>`_ for further details.
diff --git a/latest/_sources/guide/acceleration.rst.txt b/latest/_sources/guide/acceleration.rst.txt
index 2654a548a6..952ee73539 100644
--- a/latest/_sources/guide/acceleration.rst.txt
+++ b/latest/_sources/guide/acceleration.rst.txt
@@ -15,7 +15,7 @@
 .. *******************************************************************************/
 
 ########################
-Performance Tuning Guide
+Tuning Guide
 ########################
 
 The performance of some algorithms changes based on the parameters that are used.
diff --git a/latest/_sources/index.rst.txt b/latest/_sources/index.rst.txt
index 5422ba696e..4663cd1d5b 100644
--- a/latest/_sources/index.rst.txt
+++ b/latest/_sources/index.rst.txt
@@ -23,101 +23,116 @@
 |intelex|
 #########
 
-With |intelex| you can accelerate your Scikit-learn applications and
-still have full conformance with all Scikit-Learn APIs and algorithms.
-|intelex| is a free software AI accelerator that brings over 10-100X acceleration across a variety of applications.
-
-|intelex| offers you a way to accelerate existing scikit-learn code.
-The acceleration is achieved through :term:`patching`:
-replacing the stock scikit-learn algorithms with their optimized versions provided by the extension.
+Intel(R) Extension for Scikit-learn is a **free software AI accelerator** designed to deliver up to **100X** faster performance for your existing scikit-learn code.
+The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.
 
 .. rubric:: Designed for Data Scientists and Framework Designers
 
-|intelex| was created to provide data scientists with a way to get a better performance
-while using the familiar scikit-learn package and getting the same results.
 
-Usage
-------
+Use Intel(R) Extension for Scikit-learn, to:
+
+* Speed up training and inference by up to 100x with the equivalent mathematical accuracy
+* Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs
+* Integrate the extension into your existing Scikit-learn applications without code modifications
+* Enable and disable the extension with a couple of lines of code or at the command line
+
+Intel(R) Extension for Scikit-learn is also a part of `Intel(R) AI Tools <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html>`_.
+
+
+.. image:: _static/scikit-learn-acceleration.PNG
+  :width: 800
+
+
+These performance charts use benchmarks that you can find in the `scikit-learn bench repository <https://github.com/IntelPython/scikit-learn_bench>`_. 
+
+
+Supported Algorithms 
+---------------------
+
+See all of the :ref:`sklearn_algorithms`. 
+
+
+Intel(R) Optimizations
+----------------------------------
+
+Enable Intel(R) CPU Optimizations
+*********************************
+
+::
+
+   import numpy as np
+   from sklearnex import patch_sklearn
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+Enable Intel(R) GPU optimizations
+*********************************
+::
+
+   import numpy as np
+   import dpctl
+   from sklearnex import patch_sklearn, config_context
+   patch_sklearn()
+
+   from sklearn.cluster import DBSCAN
+
+   X = np.array([[1., 2.], [2., 2.], [2., 3.],
+               [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+   with config_context(target_offload="gpu:0"):
+       clustering = DBSCAN(eps=3, min_samples=2).fit(X)
 
-.. include:: usage.rst
 
-Important Links
---------------------
-- |intelex_repo|_
-- `Machine Learning Benchmarks <https://github.com/IntelPython/scikit-learn_bench>`_
-- `About Intel(R) oneAPI Data Analytics Library <https://github.com/oneapi-src/oneDAL>`_
-- `About daal4py <https://github.com/intel/scikit-learn-intelex/tree/master/daal4py>`_
 
 .. toctree::
    :caption: Get Started
-   :titlesonly: 
    :hidden:
-   :maxdepth: 1
+   :maxdepth: 3
 
    quick-start.rst
-   what-is-patching.rst
-   support.rst
+   samples.rst
+   kaggle.rst
    
 .. toctree::
-   :caption: Features
+   :caption: Developer Guide
    :hidden:
    :maxdepth: 2
 
    algorithms.rst
    oneAPI and GPU support <oneapi-gpu.rst>
    distributed-mode.rst
+   non-scikit-algorithms.rst
+   input-types.rst
+   array_api.rst
    verbose.rst
-
-.. toctree::
-   :caption: Releases
-   :hidden:
-   :maxdepth: 2
-
-   releases.rst
+   preview.rst
    deprecation.rst
-
-.. toctree::
-   :caption: Installation Guide
-   :hidden:
-   :maxdepth: 2
-
-   installation.rst
-   System Requirements <system-requirements.rst>
-   memory-requirements.rst
-
-.. toctree::
-   :caption: Examples
-   :hidden:
-   :maxdepth: 2
-
-   samples.rst
-   kaggle.rst
+   
 
 .. toctree::
    :caption: Performance
    :hidden:
    :maxdepth: 2
 
-   acceleration.rst
    guide/acceleration.rst
 
 .. toctree::
-   :caption: Blogs & Publications
+   :caption: Learn 
    :hidden:
    :maxdepth: 2
 
+   Tutorials & Case Studies <tutorials.rst>
    Medium Blogs <blogs.rst>
 
 .. toctree::
-   :caption: How to contribute
+   :caption: More
    :hidden:
    :maxdepth: 2
-
+   
+   Support <support.rst>
    contribute.rst
-
-.. toctree::
-   :caption: License
-   :hidden:
-   :maxdepth: 2
-
    license.rst
diff --git a/latest/_sources/input-types.rst.txt b/latest/_sources/input-types.rst.txt
new file mode 100644
index 0000000000..cd1714feb8
--- /dev/null
+++ b/latest/_sources/input-types.rst.txt
@@ -0,0 +1,43 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _input_types:
+
+=====================
+Supported input types
+=====================
+
+Just like Scikit-Learn estimators, estimators from Intel(R)'s Extension are able to accept
+and work with different classes of input data, including:
+
+- `Numpy <https://numpy.org/>`__ arrays.
+- Other array classes implementing the `Array API <https://data-apis.org/array-api/latest/>`__ protocol
+  (see :ref:`array_api` for details).
+- `SciPy sparse arrays and sparse matrices <https://docs.scipy.org/doc/scipy/tutorial/sparse.html>`__ (depending on the estimator).
+- `Pandas <https://pandas.pydata.org>`__ ``DataFrame`` and ``Series`` classes.
+
+In addition, Intel(R)'s Extension also supports:
+
+- `dpnp <https://github.com/IntelPython/dpnp>`__ arrays.
+- `dpctl <https://intelpython.github.io/dpctl/latest/index.html>`__ arrays.
+
+Stock Scikit-Learn estimators, depending on the version, might offer support for additional
+input types beyond this list, such as ``DataFrame`` and ``Series`` classes from other libraries
+like `Polars <https://pola.rs>`__.
+
+Intel(R)'s Extension currently does not offer accelerated routines for input types not listed
+here - when receiving an unsupported class, estimators will fall back to stock Scikit-Learn to
+handle it, so make sure to convert them to a supported type when using Intel(R)'s Extension.
diff --git a/latest/_sources/kaggle.rst.txt b/latest/_sources/kaggle.rst.txt
index a00152a403..364ab723d1 100644
--- a/latest/_sources/kaggle.rst.txt
+++ b/latest/_sources/kaggle.rst.txt
@@ -16,21 +16,25 @@
 
 .. _kaggle:
 
+####################
 Kaggle Kernels
---------------------
+####################
 
-Check out Kaggle notebooks created by |intelex| developers.
+See Kaggle notebooks created by |intelex| developers.
 
-.. rubric:: Acceleration
+Acceleration
+---------------------------------
 
 `Introduction to scikit-learn-intelex <https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook>`_
 provides a summary of the speedup you can achieve with |intelex|.
 
-.. rubric:: Machine Learning Workflows
+Machine Learning Workflows
+---------------------------------
 
 Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.
 
 Kaggle kernels that use scikit-learn and |intelex|:
+******************************************************************
 
 .. toctree::
 
@@ -38,6 +42,7 @@ Kaggle kernels that use scikit-learn and |intelex|:
    Regression Tasks <kaggle/regression.rst>
 
 Kaggle kernels that use AutoML with |intelex|:
+******************************************************************
 
 .. toctree::
 
diff --git a/latest/_sources/kaggle/classification.rst.txt b/latest/_sources/kaggle/classification.rst.txt
index 5d7b94345f..c0176cab62 100644
--- a/latest/_sources/kaggle/classification.rst.txt
+++ b/latest/_sources/kaggle/classification.rst.txt
@@ -113,7 +113,7 @@ MultiClass Classification
        - training and prediction using scikit-learn-intelex
        - search for optimal paramters using Optuna
        - performance comparison to scikit-learn
-   * - `Support Vector Classification (SVC) for MultiClass Classification
+   * - `Support Vector Classification (SVC) for MultiClass Classification (example 1)
        <https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex>`_
        
        **Data:** [TPS May 2021] Synthetic eCommerce data
@@ -135,7 +135,7 @@ MultiClass Classification
        - searching for optimal parameters for the stacking classifier
        - training and prediction using scikit-learn-intelex
        - performance comparison to scikit-learn
-   * - `Support Vector Classification (SVC) for MultiClass Classification
+   * - `Support Vector Classification (SVC) for MultiClass Classification (example 2)
        <https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup>`_
        
        **Data:** [TPS Dec 2021] Synthetic Forest Cover Type data
diff --git a/latest/_sources/license.rst.txt b/latest/_sources/license.rst.txt
index 945542761b..df68abaea6 100644
--- a/latest/_sources/license.rst.txt
+++ b/latest/_sources/license.rst.txt
@@ -20,4 +20,4 @@
 License
 ############################################
 
-License: https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE
\ No newline at end of file
+License: https://github.com/intel/scikit-learn-intelex/blob/main/LICENSE
\ No newline at end of file
diff --git a/latest/_sources/non-scikit-algorithms.rst.txt b/latest/_sources/non-scikit-algorithms.rst.txt
new file mode 100644
index 0000000000..620461843f
--- /dev/null
+++ b/latest/_sources/non-scikit-algorithms.rst.txt
@@ -0,0 +1,44 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+Non-Scikit-Learn Algorithms
+===========================
+Algorithms not presented in the original scikit-learn are described here. All algorithms are 
+available for both CPU and GPU (including distributed mode)
+
+BasicStatistics
+---------------
+.. autoclass:: sklearnex.basic_statistics.BasicStatistics
+.. automethod:: sklearnex.basic_statistics.BasicStatistics.fit
+
+IncrementalBasicStatistics
+--------------------------
+.. autoclass:: sklearnex.basic_statistics.IncrementalBasicStatistics
+.. automethod:: sklearnex.basic_statistics.IncrementalBasicStatistics.fit
+.. automethod:: sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit
+
+IncrementalEmpiricalCovariance
+------------------------------
+.. autoclass:: sklearnex.covariance.IncrementalEmpiricalCovariance
+.. automethod:: sklearnex.covariance.IncrementalEmpiricalCovariance.fit
+.. automethod:: sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit
+
+IncrementalLinearRegression
+---------------------------
+.. autoclass:: sklearnex.linear_model.IncrementalLinearRegression
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.fit
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.partial_fit
+.. automethod:: sklearnex.linear_model.IncrementalLinearRegression.predict
\ No newline at end of file
diff --git a/latest/_sources/oneapi-gpu.rst.txt b/latest/_sources/oneapi-gpu.rst.txt
index 76a2a752f4..37bfbf3a92 100644
--- a/latest/_sources/oneapi-gpu.rst.txt
+++ b/latest/_sources/oneapi-gpu.rst.txt
@@ -38,9 +38,9 @@ DPC++ compiler runtime can be installed either from PyPI or Anaconda:
 
      pip install dpcpp-cpp-rt
 
-- Install from Anaconda::
+- Install using Conda via the Intel repository::
 
-     conda install dpcpp_cpp_rt -c intel
+     conda install dpcpp_cpp_rt -c https://software.repos.intel.com/python/conda/
 
 Device offloading
 -----------------
diff --git a/latest/_sources/preview.rst.txt b/latest/_sources/preview.rst.txt
new file mode 100644
index 0000000000..7f19c6a264
--- /dev/null
+++ b/latest/_sources/preview.rst.txt
@@ -0,0 +1,73 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+.. _preview:
+
+#####################
+Preview Functionality
+#####################
+
+Some of implemented functionality in |intelex| doesn't meet one or few of next requirements
+for being enabled by default for all users:
+
+* The functionality API is not stable and can be changed in future
+* The functionality doesn't have full compatibility with stock Scikit-learn
+* The functionality misses performance targets compared to stock Scikit-learn or previously available version of functionality
+* The functionality is not fully tested
+
+This type of functionality is available under **preview mode** of |intelex| and located in
+the corresponding module (`sklearnex.preview`).
+
+Preview functionality *may* or *may not* participate in patching of Scikit-learn.
+For example, a preview estimator may be a replacement for a stock one or a completely new one.
+
+To enable preview functionality, you need to set the `SKLEARNEX_PREVIEW` environment variable
+to non-empty value before patching of Scikit-learn.
+For example, you can set the environment variable in the following way:
+
+- On Linux* OS ::
+
+     export SKLEARNEX_PREVIEW=1
+
+- On Windows* OS ::
+
+     set SKLEARNEX_PREVIEW=1
+
+Then, you can import Scikit-learn estimator patched with a preview one from `sklearnex.preview` module::
+
+     from sklearnex import patch_sklearn
+     patch_sklearn()
+     from sklearn.decomposition import IncrementalPCA
+     print(IncrementalPCA.__module__)
+     # output:
+     # sklearnex.preview.decomposition.incremental_pca
+
+Current list of preview estimators:
+
+.. list-table::
+   :widths: 30 20 10
+   :header-rows: 1
+   :align: left
+
+   * - Estimator name
+     - Module
+     - Is patching supported
+   * - EmpiricalCovariance
+     - sklearnex.preview.covariance
+     - Yes
+   * - IncrementalPCA
+     - sklearnex.preview.decomposition
+     - Yes
diff --git a/latest/_sources/quick-start.rst.txt b/latest/_sources/quick-start.rst.txt
index a7b000c7dc..bc6ac8798a 100644
--- a/latest/_sources/quick-start.rst.txt
+++ b/latest/_sources/quick-start.rst.txt
@@ -14,17 +14,392 @@
 .. * limitations under the License.
 .. *******************************************************************************/
 
-.. _quick_start:
+.. |intelex_repo| replace:: |intelex| repository
+.. _intelex_repo: https://github.com/intel/scikit-learn-intelex
 
-############################################
+####################
 Quick Start
-############################################
+####################
+
+Get ready to elevate your scikit-learn code with |intelex| and experience the benefits of accelerated performance in just a few simple steps. 
+
+Compatibility with Scikit-learn*
+---------------------------------
+
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.
+
+Integrate |intelex|
+--------------------
+
+Patching 
+**********************
+
+Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension. 
+This action is called ``patching``. This is not a permanent change so you can always undo the patching if necessary.
+
+To patch Intel® Extension for Scikit-learn, use one of these methods: 
+
+.. list-table:: 
+   :header-rows: 1
+   :align: left
+
+   * - Method
+     - Action
+   * - Use a flag in the command line
+     - Run this command:
+
+       :: 
+         
+          python -m sklearnex my_application.py
+   * - Modify your script 
+     - Add the following lines:
+
+       ::
+ 
+          from sklearnex import patch_sklearn
+          patch_sklearn()   
+   * - Import an estimator from the ``sklearnex`` module
+     - Run this command:
+
+       ::
+
+          from sklearnex.neighbors import NearestNeighbors
+
+
+
+These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.
+
+   
+**Example**
+
+This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.
+  
+.. code-block:: python
+  :caption: Example: Drop-In Patching
+   
+    import numpy as np
+    from sklearnex import patch_sklearn
+    patch_sklearn()
+
+    # You need to re-import scikit-learn algorithms after the patch
+    from sklearn.cluster import KMeans
+  
+    # The use of the original Scikit-learn is not changed
+    X = np.array([[1,  2], [1,  4], [1,  0],
+                [10, 2], [10, 4], [10, 0]])
+    kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
+    print(f"kmeans.labels_ = {kmeans.labels_}")
+
+
+Global Patching
+**********************
+
+You can also use global patching to patch all your scikit-learn applications without any additional actions.
+
+Before you begin, make sure that you have read and write permissions for Scikit-learn files. 
+
+With global patching, you can:
+
+.. list-table:: 
+   :header-rows: 1
+   :align: left
+
+   * - Task
+     - Action
+     - Note
+   * - Patch all supported algorithms
+     - Run this command:
+
+       :: 
+         
+          python -m sklearnex.glob patch_sklearn
+     
+     - If you run the global patching command several times with different parameters, then only the last configuration is applied.
+   * - Patch selected algorithms
+     - Use ``--algorithm`` or ``-a`` keys with a list of algorithms to patch. For example, to patch only ``SVC`` and ``RandomForestClassifier`` estimators, run
+
+       ::
+ 
+           python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier
+  
+     -   
+   * - Enable global patching via code
+     - Use the ``patch_sklearn`` function with the ``global_patch`` argument:
+
+       ::
+
+          from sklearnex import patch_sklearn
+          patch_sklearn(global_patch=True)
+          import sklearn
+      
+     - After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.
+   * - Disable patching notifications
+     - Use ``--no-verbose`` or ``-nv`` keys:
+
+       ::
+
+          python -m sklearnex.glob patch_sklearn -a svc random_forest_classifier -nv
+     -  
+   * - Disable global patching
+     - Run this command:
+
+       ::
+
+          python -m sklearnex.glob unpatch_sklearn
+     -
+   * - Disable global patching via code
+     - Use the ``global_patch`` argument in the ``unpatch_sklearn`` function
+
+       ::
+
+          from sklearnex import unpatch_sklearn
+          unpatch_sklearn(global_patch=True)
+     -
+    
+.. tip:: If you clone an environment with enabled global patching, it will already be applied in the new environment.
+
+Unpatching
+**********************
+
+To undo the patch (also called `unpatching`) is to return scikit-learn to original implementation and
+replace patched algorithms with the stock scikit-learn algorithms.
+
+To unpatch successfully, you must reimport the scikit-learn package::
+
+  sklearnex.unpatch_sklearn()
+  # Re-import scikit-learn algorithms after the unpatch
+  from sklearn.cluster import KMeans  
+
+
+Installation 
+--------------------
+
+.. contents:: :local:
+
+.. tip:: To prevent version conflicts, we recommend creating and activating a new environment for |intelex|. 
+
+Install from PyPI 
+**********************
+
+Recommended by default. 
+
+To install |intelex|, run:
+
+::
+
+  pip install scikit-learn-intelex
+
+**Supported Configurations**
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+
+   * - OS / Python version
+     - Python 3.9
+     - Python 3.10
+     - Python 3.11
+     - Python 3.12
+   * - Linux* OS
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+   * - Windows* OS
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+     - [CPU, GPU]
+
+
+
+Install from Anaconda* Cloud
+********************************************
+
+To prevent version conflicts, we recommend installing `scikit-learn-intelex` into a new conda environment.
+
+.. tabs::
+
+   .. tab:: Conda-Forge channel
+
+      Recommended by default. 
+      
+      To install, run::
+
+        conda install scikit-learn-intelex -c conda-forge
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+         * - Windows* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+
+
+   .. tab:: Intel channel
+
+      Recommended for the Intel® Distribution for Python users. 
+
+      To install, run::
+
+        conda install scikit-learn-intelex -c https://software.repos.intel.com/python/conda/
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+         * - Windows* OS
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+           - [CPU, GPU]
+ 
+
+
+   .. tab:: Main channel
+
+      To install, run::
+
+        conda install scikit-learn-intelex
+      
+      .. list-table:: **Supported Configurations**
+         :header-rows: 1
+         :align: left
+
+         * - OS / Python version
+           - Python 3.9
+           - Python 3.10
+           - Python 3.11
+           - Python 3.12
+         * - Linux* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+         * - Windows* OS
+           - [CPU]
+           - [CPU]
+           - [CPU]
+           - [CPU]
+
+
+
+Build from Sources
+**********************
+
+See `Installation instructions <https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md>`_ to build |intelex| from the sources.
+
+Install Intel*(R) AI Tools
+****************************
+
+Download the Intel AI Tools `here <https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html>`_. The extension is already included.
+
+Release Notes
+-------------------
+
+See the `Release Notes <https://github.com/intel/scikit-learn-intelex/releases>`_ for each version of Intel® Extension for Scikit-learn*.  
+
+System Requirements 
+--------------------
+
+Hardware Requirements
+**********************
+
+.. tabs::
+
+   .. tab:: CPU
+
+      All processors with ``x86`` architecture with at least one of the following instruction sets:
+
+        - SSE2
+        - SSE4.2
+        - AVX2
+        - AVX512
+       
+      .. note:: ARM* architecture is not supported.
+
+   .. tab:: GPU
+
+      - All Intel® integrated and discrete GPUs
+      - Intel® GPU drivers
+
+
+.. tip:: Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our :ref:`blogs <blogs>`.
+
+
+Software Requirements
+**********************
+
+.. tabs::
+
+   .. tab:: CPU
+
+      - Linux* OS: Ubuntu* 18.04 or newer
+      - Windows* OS 10 or newer
+      - Windows* Server 2019 or newer
+
+   .. tab:: GPU
+
+      - Linux* OS: Ubuntu* 18.04 or newer
+      - Windows* OS 10 or newer
+      - Windows* Server 2019 or newer
+      
+      .. important::
+         
+         If you use accelerators, refer to `oneAPI DPC++/C++ Compiler System Requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_.
+
+Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:
+
+* 1.0.X
+* 1.1.X
+* 1.2.X 
+* 1.3.X
+
+Memory Requirements
+**********************
+By default, algorithms in |intelex| run in the multi-thread mode. This mode uses all available threads. 
+Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.
+
+.. list-table::
+   :header-rows: 1
+   :align: left
+
+   * - Algorithm
+     - Single-thread mode
+     - Multi-thread mode
+   * - SVM
+     - Both Scikit-learn and |intelex| consume approximately the same amount of RAM.
+     - In |intelex|, an algorithm with ``N`` threads consumes ``N`` times more RAM.
+
+In all |intelex| algorithms with GPU support, computations run on device memory. 
+The device memory must be large enough to store a copy of the entire dataset.
+You may also require additional device memory for internal arrays that are used in computation.
 
-.. include:: usage.rst
 
 .. seealso::
 
-   - :ref:`Supported algorithms and parameters <sklearn_algorithms>`
-   - :ref:`samples`
-   - :ref:`global_patching`
-   - `Getting Started Guide <https://www.intel.com/content/www/us/en/developer/articles/guide/intel-extension-for-scikit-learn-getting-started.html>`_
+   :ref:`Samples<samples>`
diff --git a/latest/_sources/samples.rst.txt b/latest/_sources/samples.rst.txt
index 1e14a7880c..4ca25f6d92 100644
--- a/latest/_sources/samples.rst.txt
+++ b/latest/_sources/samples.rst.txt
@@ -17,12 +17,12 @@
 .. _samples:
 
 #######
-Samples
+Samples 
 #######
 
 The following samples are also provided as Jupyter notebooks in |intelex| repository.
 If you want to run them locally, refer to `these instructions
-<https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks>`_.
+<https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks>`_.
 
 .. rubric:: Classification Tasks
 
@@ -53,6 +53,13 @@ If you want to run them locally, refer to `these instructions
    Kmeans for spoken arabic digit dataset <samples/kmeans.ipynb>
    DBSCAN for spoken arabic digit dataset <samples/dbscan.ipynb>
 
+.. rubric:: Dimensionality Reduction Tasks
+
+.. toctree::
+   :maxdepth: 1
+
+   Intel® Extension for Scikit-learn TSNE example <samples/tsne.ipynb>
+
 .. rubric:: Jupyter Notebook Samples
 
 .. toctree::
@@ -65,4 +72,4 @@ If you want to run them locally, refer to `these instructions
 
    There are also :ref:`Kaggle kernels <kaggle>` that use |intelex| for a variety of machine learning scenarios.
 
-.. note:: Explore the complete list of oneAPI code samples in the `oneAPI Samples Catalog <https://oneapi-src.github.io/oneAPI-samples/>`_. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.
\ No newline at end of file
+.. note:: Explore the complete list of oneAPI code samples in the `oneAPI Samples Catalog <https://oneapi-src.github.io/oneAPI-samples/>`_. These samples were designed to help you develop, offload, and optimize multiarchitecture applications targeting CPUs, GPUs, and FPGAs.
diff --git a/latest/_sources/samples/ElasticNet.ipynb.txt b/latest/_sources/samples/ElasticNet.ipynb.txt
index 9ffdd8fe1c..29e5dca851 100644
--- a/latest/_sources/samples/ElasticNet.ipynb.txt
+++ b/latest/_sources/samples/ElasticNet.ipynb.txt
@@ -164,7 +164,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/samples/dbscan.ipynb.txt b/latest/_sources/samples/dbscan.ipynb.txt
index 829719629a..d6e5c92653 100644
--- a/latest/_sources/samples/dbscan.ipynb.txt
+++ b/latest/_sources/samples/dbscan.ipynb.txt
@@ -128,7 +128,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/samples/kmeans.ipynb.txt b/latest/_sources/samples/kmeans.ipynb.txt
index 8e6470f568..df09f8ded5 100644
--- a/latest/_sources/samples/kmeans.ipynb.txt
+++ b/latest/_sources/samples/kmeans.ipynb.txt
@@ -139,7 +139,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/samples/knn_mnist.ipynb.txt b/latest/_sources/samples/knn_mnist.ipynb.txt
index 6b7e2465a0..b8604d70f0 100644
--- a/latest/_sources/samples/knn_mnist.ipynb.txt
+++ b/latest/_sources/samples/knn_mnist.ipynb.txt
@@ -104,7 +104,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/samples/lasso_regression.ipynb.txt b/latest/_sources/samples/lasso_regression.ipynb.txt
index 77571c2432..967d0d4e54 100644
--- a/latest/_sources/samples/lasso_regression.ipynb.txt
+++ b/latest/_sources/samples/lasso_regression.ipynb.txt
@@ -162,7 +162,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/samples/linear_regression.ipynb.txt b/latest/_sources/samples/linear_regression.ipynb.txt
index 4a356e6bfc..508ee06d8c 100644
--- a/latest/_sources/samples/linear_regression.ipynb.txt
+++ b/latest/_sources/samples/linear_regression.ipynb.txt
@@ -162,7 +162,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/samples/logistictic_regression_cifar.ipynb.txt b/latest/_sources/samples/logistictic_regression_cifar.ipynb.txt
index f4cb2b95a7..43727804d7 100644
--- a/latest/_sources/samples/logistictic_regression_cifar.ipynb.txt
+++ b/latest/_sources/samples/logistictic_regression_cifar.ipynb.txt
@@ -108,7 +108,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/samples/nusvr_medical_charges.ipynb.txt b/latest/_sources/samples/nusvr_medical_charges.ipynb.txt
index bcfbe9154f..8c72c1b71d 100644
--- a/latest/_sources/samples/nusvr_medical_charges.ipynb.txt
+++ b/latest/_sources/samples/nusvr_medical_charges.ipynb.txt
@@ -133,7 +133,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/samples/random_forest_yolanda.ipynb.txt b/latest/_sources/samples/random_forest_yolanda.ipynb.txt
index a184b22fd1..276284ed9b 100644
--- a/latest/_sources/samples/random_forest_yolanda.ipynb.txt
+++ b/latest/_sources/samples/random_forest_yolanda.ipynb.txt
@@ -104,7 +104,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/samples/ridge_regression.ipynb.txt b/latest/_sources/samples/ridge_regression.ipynb.txt
index c3d0856d00..1c159a13ae 100644
--- a/latest/_sources/samples/ridge_regression.ipynb.txt
+++ b/latest/_sources/samples/ridge_regression.ipynb.txt
@@ -169,7 +169,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
   {
diff --git a/latest/_sources/support.rst.txt b/latest/_sources/support.rst.txt
index 15d495c864..03a2dddadb 100644
--- a/latest/_sources/support.rst.txt
+++ b/latest/_sources/support.rst.txt
@@ -14,12 +14,21 @@
 .. * limitations under the License.
 .. *******************************************************************************/
 
-Support
---------------------
-Report issues, ask questions, and provide suggestions using:
+######################################################
+Intel(R) Extension for Scikit-learn Support
+######################################################
 
-- `GitHub Issues <https://github.com/intel/scikit-learn-intelex/issues>`_
-- `GitHub Discussions <https://github.com/intel/scikit-learn-intelex/discussions>`_
-- `Forum <https://community.intel.com/t5/Intel-Distribution-for-Python/bd-p/distribution-python>`_
 
-You may reach out to project maintainers privately at onedal.maintainers@intel.com
+We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn. 
+
+Use the following methods if you face any challenges. 
+
+
+Issues
+----------------------------------
+
+If you have a problem, check out the `GitHub Issues <https://github.com/intel/scikit-learn-intelex/issues>`_ to see if the issue you want to address is already reported. 
+
+You may find users that have encountered the same bug or have similar ideas for changes or updates.
+
+You can use issues to report a problem, make a feature request, or add comments on an existing issue.
diff --git a/latest/_sources/tutorials.rst.txt b/latest/_sources/tutorials.rst.txt
new file mode 100644
index 0000000000..ce778bb5ab
--- /dev/null
+++ b/latest/_sources/tutorials.rst.txt
@@ -0,0 +1,92 @@
+.. ******************************************************************************
+.. * Copyright 2024 Intel Corporation
+.. *
+.. * Licensed under the Apache License, Version 2.0 (the "License");
+.. * you may not use this file except in compliance with the License.
+.. * You may obtain a copy of the License at
+.. *
+.. *     http://www.apache.org/licenses/LICENSE-2.0
+.. *
+.. * Unless required by applicable law or agreed to in writing, software
+.. * distributed under the License is distributed on an "AS IS" BASIS,
+.. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. * See the License for the specific language governing permissions and
+.. * limitations under the License.
+.. *******************************************************************************/
+
+
+################################################
+|intelex| Tutorials & Case Studies
+################################################
+
+Explore the tutorials & case studies to help you to harness the full potential of |intelex|.
+
+Tutorials
+----------------------------------
+
+.. grid:: 3
+
+   .. grid-item-card:: Introduction to scikit-learn* Essentials for Machine Learning
+      :link: https://www.intel.com/content/www/us/en/developer/videos/introduction-to-scikit-learn-essentials-for-ml.html
+      :padding: 1
+
+      An overview of scikit-learn essentials.
+
+   .. grid-item-card:: Advanced scikit-learn* Essentials for Machine Learning
+      :link: https://www.intel.com/content/www/us/en/developer/videos/advanced-scikit-learn-essentials-for-ml.html
+      :padding: 1
+      
+      Special technique to perform scikit-learn computation on Intel GPUs. 
+   
+   .. grid-item-card:: Develop Efficient AI Solutions with Accelerated Machine Learning
+      :link: https://www.intel.com/content/www/us/en/developer/videos/accelerated-machine-learning-for-ai-solutions.html
+      :padding: 1
+
+      Techniques for maximizing Intel® Extension for Scikit-learn*. 
+
+   .. grid-item-card:: Getting started with classical Machine Learning Frameworks using Google Colaboratory
+      :link: https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Getting-started-with-classical-Machine-Learning-Frameworks-using/post/1450139
+      :padding: 1
+
+      Simple Installation of Intel® Extension for Scikit-learn* on Google Colaboratory.     
+
+   .. grid-item-card:: Accelerate Machine Learning Workloads: K-means and GPairs Algorithms
+      :link: https://www.intel.com/content/www/us/en/developer/videos/accelerate-ml-workloads-k-means-gpairs-algorithms.html
+      :padding: 1
+
+      An introduction to Intel® Extension for Scikit-learn*.
+
+   .. grid-item-card:: Benchmarking Intel® Extension for Scikit-learn*: How Much Faster Is It?
+      :link: https://www.intel.com/content/www/us/en/developer/articles/technical/benchmarking-intel-extension-for-scikit-learn.html
+      :padding: 1
+
+      Explore and compare the performance of the Intel Extension for Scikit-learn* vs. stock Scikit-learn*.
+
+   .. grid-item-card:: Drive Innovation & Performance into Your scikit-learn* Machine Learning Tasks
+      :link: https://www.intel.com/content/www/us/en/developer/videos/drive-innovation-performance-scikit-learn-ml-tasks.html
+      :padding: 1
+
+      An overview of scikit-learn essentials.
+
+Case Studies 
+----------------------------------
+
+.. grid:: 3
+
+   .. grid-item-card:: Greener Machine Learning Computing with Intel® AI Acceleration
+      :link: https://www.intel.com/content/www/us/en/developer/articles/case-study/greener-machine-learning-with-intel-ai.html
+      :padding: 1
+
+      Lowering the Environmental Impact of AI Workloads by 7x using Intel® Extension for Scikit-learn*.
+
+   .. grid-item-card:: Supply Chain Optimization at Enterprise Scale
+      :link: https://www.intel.com/content/www/us/en/developer/articles/technical/supply-chain-optimization-at-enterprise-scale.html
+      :padding: 1
+
+      Leveraging Open-Source AI Technologies with Red Hat OpenShift* Data Science and Intel® Architecture.
+
+   .. grid-item-card:: HippoScreen Improves AI Performance by 2.4x with oneAPI Tools
+      :link: https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html
+      :padding: 1
+
+      The neurotechnology startup used |intelex| to the improve efficiency and build times of deep-learning models used in its Brain Waves AI system.
\ No newline at end of file
diff --git a/latest/_sources/verbose.rst.txt b/latest/_sources/verbose.rst.txt
index 1eee3c6e4b..cf77cc0669 100644
--- a/latest/_sources/verbose.rst.txt
+++ b/latest/_sources/verbose.rst.txt
@@ -27,11 +27,11 @@ Use |intelex| in verbose mode to find out which implementation of the algorithm
 
 To enable verbose mode, set the ``SKLEARNEX_VERBOSE`` environment variable as shown below:
 
-- On Linux and MacOS::
+- On Linux* OS ::
 
      export SKLEARNEX_VERBOSE=INFO
 
-- On Windows::
+- On Windows* OS ::
 
      set SKLEARNEX_VERBOSE=INFO
 
diff --git a/latest/_sphinx_design_static/design-tabs.js b/latest/_sphinx_design_static/design-tabs.js
new file mode 100644
index 0000000000..b25bd6a4fa
--- /dev/null
+++ b/latest/_sphinx_design_static/design-tabs.js
@@ -0,0 +1,101 @@
+// @ts-check
+
+// Extra JS capability for selected tabs to be synced
+// The selection is stored in local storage so that it persists across page loads.
+
+/**
+ * @type {Record<string, HTMLElement[]>}
+ */
+let sd_id_to_elements = {};
+const storageKeyPrefix = "sphinx-design-tab-id-";
+
+/**
+ * Create a key for a tab element.
+ * @param {HTMLElement} el - The tab element.
+ * @returns {[string, string, string] | null} - The key.
+ *
+ */
+function create_key(el) {
+  let syncId = el.getAttribute("data-sync-id");
+  let syncGroup = el.getAttribute("data-sync-group");
+  if (!syncId || !syncGroup) return null;
+  return [syncGroup, syncId, syncGroup + "--" + syncId];
+}
+
+/**
+ * Initialize the tab selection.
+ *
+ */
+function ready() {
+  // Find all tabs with sync data
+
+  /** @type {string[]} */
+  let groups = [];
+
+  document.querySelectorAll(".sd-tab-label").forEach((label) => {
+    if (label instanceof HTMLElement) {
+      let data = create_key(label);
+      if (data) {
+        let [group, id, key] = data;
+
+        // add click event listener
+        // @ts-ignore
+        label.onclick = onSDLabelClick;
+
+        // store map of key to elements
+        if (!sd_id_to_elements[key]) {
+          sd_id_to_elements[key] = [];
+        }
+        sd_id_to_elements[key].push(label);
+
+        if (groups.indexOf(group) === -1) {
+          groups.push(group);
+          // Check if a specific tab has been selected via URL parameter
+          const tabParam = new URLSearchParams(window.location.search).get(
+            group
+          );
+          if (tabParam) {
+            console.log(
+              "sphinx-design: Selecting tab id for group '" +
+                group +
+                "' from URL parameter: " +
+                tabParam
+            );
+            window.sessionStorage.setItem(storageKeyPrefix + group, tabParam);
+          }
+        }
+
+        // Check is a specific tab has been selected previously
+        let previousId = window.sessionStorage.getItem(
+          storageKeyPrefix + group
+        );
+        if (previousId === id) {
+          // console.log(
+          //   "sphinx-design: Selecting tab from session storage: " + id
+          // );
+          // @ts-ignore
+          label.previousElementSibling.checked = true;
+        }
+      }
+    }
+  });
+}
+
+/**
+ *  Activate other tabs with the same sync id.
+ *
+ * @this {HTMLElement} - The element that was clicked.
+ */
+function onSDLabelClick() {
+  let data = create_key(this);
+  if (!data) return;
+  let [group, id, key] = data;
+  for (const label of sd_id_to_elements[key]) {
+    if (label === this) continue;
+    // @ts-ignore
+    label.previousElementSibling.checked = true;
+  }
+  window.sessionStorage.setItem(storageKeyPrefix + group, id);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/latest/_sphinx_design_static/sphinx-design.min.css b/latest/_sphinx_design_static/sphinx-design.min.css
new file mode 100644
index 0000000000..860c36da0f
--- /dev/null
+++ b/latest/_sphinx_design_static/sphinx-design.min.css
@@ -0,0 +1 @@
+.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) !important}button.sd-bg-secondary:focus,button.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}a.sd-bg-secondary:focus,a.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}.sd-bg-success{background-color:var(--sd-color-success) !important}.sd-bg-text-success{color:var(--sd-color-success-text) !important}button.sd-bg-success:focus,button.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) 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0;position:relative}.sd-tab-set>input{opacity:0;position:absolute}.sd-tab-set>input:checked+label{border-color:var(--sd-color-tabs-underline-active);color:var(--sd-color-tabs-label-active)}.sd-tab-set>input:checked+label+.sd-tab-content{display:block}.sd-tab-set>input:not(:checked)+label:hover{color:var(--sd-color-tabs-label-hover);border-color:var(--sd-color-tabs-underline-hover)}.sd-tab-set>input:focus+label{outline-style:auto}.sd-tab-set>input:not(.focus-visible)+label{outline:none;-webkit-tap-highlight-color:transparent}.sd-tab-set>label{border-bottom:.125rem solid transparent;margin-bottom:0;color:var(--sd-color-tabs-label-inactive);border-color:var(--sd-color-tabs-underline-inactive);cursor:pointer;font-size:var(--sd-fontsize-tabs-label);font-weight:700;padding:1em 1.25em .5em;transition:color 250ms;width:auto;z-index:1}html .sd-tab-set>label:hover{color:var(--sd-color-tabs-label-active)}.sd-col>.sd-tab-set{width:100%}.sd-tab-content{box-shadow:0 -0.0625rem var(--sd-color-tabs-overline),0 .0625rem var(--sd-color-tabs-underline);display:none;order:99;padding-bottom:.75rem;padding-top:.75rem;width:100%}.sd-tab-content>:first-child{margin-top:0 !important}.sd-tab-content>:last-child{margin-bottom:0 !important}.sd-tab-content>.sd-tab-set{margin:0}.sd-sphinx-override,.sd-sphinx-override *{-moz-box-sizing:border-box;-webkit-box-sizing:border-box;box-sizing:border-box}.sd-sphinx-override p{margin-top:0}:root{--sd-color-primary: #0071bc;--sd-color-secondary: #6c757d;--sd-color-success: #28a745;--sd-color-info: #17a2b8;--sd-color-warning: #f0b37e;--sd-color-danger: #dc3545;--sd-color-light: #f8f9fa;--sd-color-muted: #6c757d;--sd-color-dark: #212529;--sd-color-black: black;--sd-color-white: white;--sd-color-primary-highlight: #0060a0;--sd-color-secondary-highlight: #5c636a;--sd-color-success-highlight: #228e3b;--sd-color-info-highlight: #148a9c;--sd-color-warning-highlight: #cc986b;--sd-color-danger-highlight: #bb2d3b;--sd-color-light-highlight: #d3d4d5;--sd-color-muted-highlight: #5c636a;--sd-color-dark-highlight: #1c1f23;--sd-color-black-highlight: black;--sd-color-white-highlight: #d9d9d9;--sd-color-primary-bg: rgba(0, 113, 188, 0.2);--sd-color-secondary-bg: rgba(108, 117, 125, 0.2);--sd-color-success-bg: rgba(40, 167, 69, 0.2);--sd-color-info-bg: rgba(23, 162, 184, 0.2);--sd-color-warning-bg: rgba(240, 179, 126, 0.2);--sd-color-danger-bg: rgba(220, 53, 69, 0.2);--sd-color-light-bg: rgba(248, 249, 250, 0.2);--sd-color-muted-bg: rgba(108, 117, 125, 0.2);--sd-color-dark-bg: rgba(33, 37, 41, 0.2);--sd-color-black-bg: rgba(0, 0, 0, 0.2);--sd-color-white-bg: rgba(255, 255, 255, 0.2);--sd-color-primary-text: #fff;--sd-color-secondary-text: #fff;--sd-color-success-text: #fff;--sd-color-info-text: #fff;--sd-color-warning-text: #212529;--sd-color-danger-text: #fff;--sd-color-light-text: #212529;--sd-color-muted-text: #fff;--sd-color-dark-text: #fff;--sd-color-black-text: #fff;--sd-color-white-text: #212529;--sd-color-shadow: rgba(0, 0, 0, 0.15);--sd-color-card-border: rgba(0, 0, 0, 0.125);--sd-color-card-border-hover: hsla(231, 99%, 66%, 1);--sd-color-card-background: transparent;--sd-color-card-text: inherit;--sd-color-card-header: transparent;--sd-color-card-footer: transparent;--sd-color-tabs-label-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-hover: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-inactive: hsl(0, 0%, 66%);--sd-color-tabs-underline-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-underline-hover: rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem;--sd-fontsize-dropdown: inherit;--sd-fontsize-dropdown-title: 1rem;--sd-fontweight-dropdown-title: 700}
diff --git a/latest/_static/_sphinx_javascript_frameworks_compat.js b/latest/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000000..81415803ec
--- /dev/null
+++ b/latest/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/latest/_static/basic.css b/latest/_static/basic.css
index 7577acb1ad..7ebbd6d07b 100644
--- a/latest/_static/basic.css
+++ b/latest/_static/basic.css
@@ -1,12 +1,5 @@
 /*
- * basic.css
- * ~~~~~~~~~
- *
  * Sphinx stylesheet -- basic theme.
- *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
  */
 
 /* -- main layout ----------------------------------------------------------- */
@@ -115,15 +108,11 @@ img {
 /* -- search page ----------------------------------------------------------- */
 
 ul.search {
-    margin: 10px 0 0 20px;
-    padding: 0;
+    margin-top: 10px;
 }
 
 ul.search li {
-    padding: 5px 0 5px 20px;
-    background-image: url(file.png);
-    background-repeat: no-repeat;
-    background-position: 0 7px;
+    padding: 5px 0;
 }
 
 ul.search li a {
@@ -237,6 +226,10 @@ a.headerlink {
     visibility: hidden;
 }
 
+a:visited {
+    color: #551A8B;
+}
+
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -670,6 +663,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -738,6 +741,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {
diff --git a/latest/_static/check-solid.svg b/latest/_static/check-solid.svg
new file mode 100644
index 0000000000..92fad4b5c0
--- /dev/null
+++ b/latest/_static/check-solid.svg
@@ -0,0 +1,4 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>
diff --git a/latest/_static/clipboard.min.js b/latest/_static/clipboard.min.js
new file mode 100644
index 0000000000..54b3c46381
--- /dev/null
+++ b/latest/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},i="";return"string"==typeof t?(e=t,n="rtl"===document.documentElement.getAttribute("dir"),(o=document.createElement("textarea")).style.fontSize="12pt",o.style.border="0",o.style.padding="0",o.style.margin="0",o.style.position="absolute",o.style[n?"right":"left"]="-9999px",n=window.pageYOffset||document.documentElement.scrollTop,o.style.top="".concat(n,"px"),o.setAttribute("readonly",""),o.value=e,o=o,r.container.appendChild(o),i=c()(o),a("copy"),o.remove()):(i=c()(t),a("copy")),i};function r(t){return(r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==r(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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You can\'t cut text from elements with "readonly" or "disabled" attributes')}return t?l(t,{container:o}):e?"cut"===n?f(e):l(e,{container:o}):void 0};function d(t){return(d="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function p(t,e){for(var n=0;n<e.length;n++){var o=e[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(t,o.key,o)}}function y(t,e){return(y=Object.setPrototypeOf||function(t,e){return t.__proto__=e,t})(t,e)}function h(n){var o=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Date.prototype.toString.call(Reflect.construct(Date,[],function(){})),!0}catch(t){return!1}}();return function(){var t,e=m(n);return t=o?(t=m(this).constructor,Reflect.construct(e,arguments,t)):e.apply(this,arguments),e=this,!(t=t)||"object"!==d(t)&&"function"!=typeof t?function(t){if(void 0!==t)return t;throw new ReferenceError("this hasn't been initialised - super() hasn't been called")}(e):t}}function m(t){return(m=Object.setPrototypeOf?Object.getPrototypeOf:function(t){return t.__proto__||Object.getPrototypeOf(t)})(t)}function v(t,e){t="data-clipboard-".concat(t);if(e.hasAttribute(t))return e.getAttribute(t)}var o=function(){!function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),e&&y(t,e)}(r,i());var t,e,n,o=h(r);function r(t,e){var n;return function(t){if(!(t instanceof r))throw new TypeError("Cannot call a class as a function")}(this),(n=o.call(this)).resolveOptions(e),n.listenClick(t),n}return t=r,n=[{key:"copy",value:function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body};return l(t,e)}},{key:"cut",value:function(t){return f(t)}},{key:"isSupported",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:["copy","cut"],t="string"==typeof t?[t]:t,e=!!document.queryCommandSupported;return t.forEach(function(t){e=e&&!!document.queryCommandSupported(t)}),e}}],(e=[{key:"resolveOptions",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};this.action="function"==typeof t.action?t.action:this.defaultAction,this.target="function"==typeof t.target?t.target:this.defaultTarget,this.text="function"==typeof t.text?t.text:this.defaultText,this.container="object"===d(t.container)?t.container:document.body}},{key:"listenClick",value:function(t){var e=this;this.listener=u()(t,"click",function(t){return e.onClick(t)})}},{key:"onClick",value:function(t){var e=t.delegateTarget||t.currentTarget,t=s({action:this.action(e),container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:this.action,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&p(t.prototype,e),n&&p(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function 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\ No newline at end of file
diff --git a/latest/_static/copy-button.svg b/latest/_static/copy-button.svg
new file mode 100644
index 0000000000..9c074dae52
--- /dev/null
+++ b/latest/_static/copy-button.svg
@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>
diff --git a/latest/_static/copybutton.css b/latest/_static/copybutton.css
new file mode 100644
index 0000000000..f1916ec7d1
--- /dev/null
+++ b/latest/_static/copybutton.css
@@ -0,0 +1,94 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .3em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+    color: #22863a;
+}
+
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/latest/_static/copybutton.js b/latest/_static/copybutton.js
new file mode 100644
index 0000000000..2ea7ff3e21
--- /dev/null
+++ b/latest/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_success']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_to_clipboard']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>`
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      ${iconCopy}
+    </button>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/latest/_static/copybutton_funcs.js b/latest/_static/copybutton_funcs.js
new file mode 100644
index 0000000000..dbe1aaad79
--- /dev/null
+++ b/latest/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/latest/_static/css/badge_only.css b/latest/_static/css/badge_only.css
index c718cee441..88ba55b965 100644
--- a/latest/_static/css/badge_only.css
+++ b/latest/_static/css/badge_only.css
@@ -1 +1 @@
-.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
\ No newline at end of file
+.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions .rst-other-versions .rtd-current-item{font-weight:700}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}#flyout-search-form{padding:6px}
\ No newline at end of file
diff --git a/latest/_static/css/theme.css b/latest/_static/css/theme.css
index c03c88f06c..0f14f10646 100644
--- a/latest/_static/css/theme.css
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diff --git a/latest/_static/custom.css b/latest/_static/custom.css
index b34fb6d5aa..1db55bd294 100644
--- a/latest/_static/custom.css
+++ b/latest/_static/custom.css
@@ -1,25 +1,131 @@
-/* make the page 1000px */
-.wy-nav-content {
-   max-width: 1200px;
-}
-
-/* code block highlight color in rtd changed to lime green, no no no */
-
-.rst-content tt.literal, .rst-content code.literal, .highlight {
-   background: #f0f0f0;
-}
-.rst-content tt.literal, .rst-content code.literal {
-    color: #000000;
-}
-/* set color of version navigator to yellow */
-div.version a {
-    color: hsla(0,0%,100%,.3);
-}
-div.version p {
-    color: #FFFF00;
-}
-section {
-    display:block;
-    float: left;
-    top: 0;
-}
+/* override table width restrictions */
+@media screen and (min-width: 767px) {
+
+   .wy-table-responsive table td {
+      /* !important prevents the common CSS stylesheets from overriding
+         this as on RTD they are loaded after this stylesheet */
+
+      white-space: normal !important;
+   }
+}
+
+.code-block-caption {
+    color: #000;
+    font: italic 85%/1 arial,sans-serif;
+    padding: 1em 0;
+    text-align: center;
+}
+
+
+.collapsible {
+  margin-left: -10px;
+  background-color: #f1f1f1;
+  cursor: pointer;
+  padding: 18px 18px 18px 10px;
+  width: 100%;
+  border: none;
+  text-align: left;
+  outline: none;
+  font-weight: 700;
+  font-family: "Roboto Slab","ff-tisa-web-pro","Georgia",Arial,sans-serif;
+}
+
+code.sig-name.descname {
+   white-space: initial;
+}
+
+table.optimization-notice td {
+   white-space: initial;
+}
+
+table.optimization-notice td p:last-child {
+   text-align: right;
+}
+
+img.with-border {
+   border:1px solid #021a40;
+}
+
+div.column {
+   float: left;
+   width: 50%;
+   padding: 10px;
+ }
+
+/* Clear floats after the columns */
+.column:after {
+ content: "";
+ display: table;
+ clear: both;
+}
+
+.comparison:after {
+   content: "";
+   display: table;
+   clear: both;
+  }
+
+div.admonition-container {
+ width: 49%;
+ padding: 0 3px 0 0;
+}
+
+/* Clear floats after the columns */
+.admonition-container:after {
+ content: "";
+ display: table;
+ clear: both;
+}
+
+div.quotation {
+  background-color: #fffff1;
+  padding: 1em 0 0 1em;
+}
+
+.rst-content div[class^='highlight'] pre {
+   white-space: pre-wrap;
+}
+
+/* A workaround for https://github.com/readthedocs/sphinx_rtd_theme/issues/647
+ * Override display for function signatures so that there is spacing between
+ * types and arguments */
+ .rst-content dl:not(.docutils) dt {
+   display: table-cell !important;
+}
+.rst-content dl:not(.docutils) dd {
+   margin-top: 6px;
+}
+
+/*
+.rst-content tt.literal, .rst-content code.literal, .highlight {
+   background: #f0f0f0;
+}
+.rst-content tt.literal, .rst-content code.literal {
+    color: #000000;
+}*/
+
+
+.eqno {
+   margin-left: 5px;
+   float: right;
+}
+.math .headerlink {
+   display: none;
+   visibility: hidden;
+}
+.math:hover .headerlink {
+   display: inline-block;
+   visibility: visible;
+   margin-right: -0.7em;
+}
+
+a#wap_dns {display: none;}
+
+
+button#version-switcher-button {
+   background-color:  #2980b9;
+}
+
+div#version-switcher-dropdown {
+   display: inline;
+}
diff --git a/latest/_static/design-tabs.js b/latest/_static/design-tabs.js
new file mode 100644
index 0000000000..b25bd6a4fa
--- /dev/null
+++ b/latest/_static/design-tabs.js
@@ -0,0 +1,101 @@
+// @ts-check
+
+// Extra JS capability for selected tabs to be synced
+// The selection is stored in local storage so that it persists across page loads.
+
+/**
+ * @type {Record<string, HTMLElement[]>}
+ */
+let sd_id_to_elements = {};
+const storageKeyPrefix = "sphinx-design-tab-id-";
+
+/**
+ * Create a key for a tab element.
+ * @param {HTMLElement} el - The tab element.
+ * @returns {[string, string, string] | null} - The key.
+ *
+ */
+function create_key(el) {
+  let syncId = el.getAttribute("data-sync-id");
+  let syncGroup = el.getAttribute("data-sync-group");
+  if (!syncId || !syncGroup) return null;
+  return [syncGroup, syncId, syncGroup + "--" + syncId];
+}
+
+/**
+ * Initialize the tab selection.
+ *
+ */
+function ready() {
+  // Find all tabs with sync data
+
+  /** @type {string[]} */
+  let groups = [];
+
+  document.querySelectorAll(".sd-tab-label").forEach((label) => {
+    if (label instanceof HTMLElement) {
+      let data = create_key(label);
+      if (data) {
+        let [group, id, key] = data;
+
+        // add click event listener
+        // @ts-ignore
+        label.onclick = onSDLabelClick;
+
+        // store map of key to elements
+        if (!sd_id_to_elements[key]) {
+          sd_id_to_elements[key] = [];
+        }
+        sd_id_to_elements[key].push(label);
+
+        if (groups.indexOf(group) === -1) {
+          groups.push(group);
+          // Check if a specific tab has been selected via URL parameter
+          const tabParam = new URLSearchParams(window.location.search).get(
+            group
+          );
+          if (tabParam) {
+            console.log(
+              "sphinx-design: Selecting tab id for group '" +
+                group +
+                "' from URL parameter: " +
+                tabParam
+            );
+            window.sessionStorage.setItem(storageKeyPrefix + group, tabParam);
+          }
+        }
+
+        // Check is a specific tab has been selected previously
+        let previousId = window.sessionStorage.getItem(
+          storageKeyPrefix + group
+        );
+        if (previousId === id) {
+          // console.log(
+          //   "sphinx-design: Selecting tab from session storage: " + id
+          // );
+          // @ts-ignore
+          label.previousElementSibling.checked = true;
+        }
+      }
+    }
+  });
+}
+
+/**
+ *  Activate other tabs with the same sync id.
+ *
+ * @this {HTMLElement} - The element that was clicked.
+ */
+function onSDLabelClick() {
+  let data = create_key(this);
+  if (!data) return;
+  let [group, id, key] = data;
+  for (const label of sd_id_to_elements[key]) {
+    if (label === this) continue;
+    // @ts-ignore
+    label.previousElementSibling.checked = true;
+  }
+  window.sessionStorage.setItem(storageKeyPrefix + group, id);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/latest/_static/doctools.js b/latest/_static/doctools.js
index d06a71d751..0398ebb9f0 100644
--- a/latest/_static/doctools.js
+++ b/latest/_static/doctools.js
@@ -1,12 +1,5 @@
 /*
- * doctools.js
- * ~~~~~~~~~~~
- *
  * Base JavaScript utilities for all Sphinx HTML documentation.
- *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
  */
 "use strict";
 
diff --git a/latest/_static/documentation_options.js b/latest/_static/documentation_options.js
index 2883fc35a2..a70a1c083b 100644
--- a/latest/_static/documentation_options.js
+++ b/latest/_static/documentation_options.js
@@ -1,6 +1,5 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '2023.2',
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '2025.1',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/latest/_static/fonts/Lato/lato-bold.eot b/latest/_static/fonts/Lato/lato-bold.eot
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-!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3-pre",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b),"object"==typeof module&&module.exports&&(module.exports=t)}("undefined"!=typeof window?window:this,document);
\ No newline at end of file
diff --git a/latest/_static/js/versions.js b/latest/_static/js/versions.js
new file mode 100644
index 0000000000..4958195e07
--- /dev/null
+++ b/latest/_static/js/versions.js
@@ -0,0 +1,228 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = true;
+const themeLanguageSelector = true;
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    // Insert the current language to the options on the selector
+    let languages = config.projects.translations.concat(config.projects.current);
+    languages = languages.sort((a, b) => a.language.name.localeCompare(b.language.name));
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${languages
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/latest/_static/language_data.js b/latest/_static/language_data.js
index 250f5665fa..c7fe6c6faf 100644
--- a/latest/_static/language_data.js
+++ b/latest/_static/language_data.js
@@ -1,19 +1,12 @@
 /*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
  */
 
 var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
-/* Non-minified version is copied as a separate JS file, is available */
+/* Non-minified version is copied as a separate JS file, if available */
 
 /**
  * Porter Stemmer
diff --git a/latest/_static/nbsphinx-code-cells.css b/latest/_static/nbsphinx-code-cells.css
index 199fa5a42f..a3fb27c30f 100644
--- a/latest/_static/nbsphinx-code-cells.css
+++ b/latest/_static/nbsphinx-code-cells.css
@@ -62,12 +62,16 @@ div.nblast.container {
 }
 
 /* input prompt */
-div.nbinput.container div.prompt pre {
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
     color: #307FC1;
 }
 
 /* output prompt */
-div.nboutput.container div.prompt pre {
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
     color: #BF5B3D;
 }
 
@@ -204,8 +208,10 @@ div.nboutput.container div.output_area > div[class^='highlight']{
     overflow-y: hidden;
 }
 
-/* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */
-.prompt .copybtn {
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
     display: none;
 }
 
diff --git a/latest/_static/pygments.css b/latest/_static/pygments.css
index 691aeb82d0..0d49244eda 100644
--- a/latest/_static/pygments.css
+++ b/latest/_static/pygments.css
@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #FF0000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #00A000 } /* Generic.Inserted */
diff --git a/latest/_static/scikit-learn-acceleration.PNG b/latest/_static/scikit-learn-acceleration.PNG
new file mode 100644
index 0000000000..d045478f39
Binary files /dev/null and b/latest/_static/scikit-learn-acceleration.PNG differ
diff --git a/latest/_static/searchtools.js b/latest/_static/searchtools.js
index 97d56a74d8..2c774d17af 100644
--- a/latest/_static/searchtools.js
+++ b/latest/_static/searchtools.js
@@ -1,12 +1,5 @@
 /*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
  * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
  */
 "use strict";
 
@@ -20,7 +13,7 @@ if (typeof Scorer === "undefined") {
     // and returns the new score.
     /*
     score: result => {
-      const [docname, title, anchor, descr, score, filename] = result
+      const [docname, title, anchor, descr, score, filename, kind] = result
       return score
     },
     */
@@ -47,6 +40,14 @@ if (typeof Scorer === "undefined") {
   };
 }
 
+// Global search result kind enum, used by themes to style search results.
+class SearchResultKind {
+    static get index() { return  "index"; }
+    static get object() { return "object"; }
+    static get text() { return "text"; }
+    static get title() { return "title"; }
+}
+
 const _removeChildren = (element) => {
   while (element && element.lastChild) element.removeChild(element.lastChild);
 };
@@ -57,16 +58,20 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, searchTerms) => {
+const _displayItem = (item, searchTerms, highlightTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
 
-  const [docName, title, anchor, descr, score, _filename] = item;
+  const [docName, title, anchor, descr, score, _filename, kind] = item;
 
   let listItem = document.createElement("li");
+  // Add a class representing the item's type:
+  // can be used by a theme's CSS selector for styling
+  // See SearchResultKind for the class names.
+  listItem.classList.add(`kind-${kind}`);
   let requestUrl;
   let linkUrl;
   if (docBuilder === "dirhtml") {
@@ -75,28 +80,35 @@ const _displayItem = (item, searchTerms) => {
     if (dirname.match(/\/index\/$/))
       dirname = dirname.substring(0, dirname.length - 6);
     else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
+    requestUrl = contentRoot + dirname;
     linkUrl = requestUrl;
   } else {
     // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
+    requestUrl = contentRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
   let linkEl = listItem.appendChild(document.createElement("a"));
   linkEl.href = linkUrl + anchor;
   linkEl.dataset.score = score;
   linkEl.innerHTML = title;
-  if (descr)
+  if (descr) {
     listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
   else if (showSearchSummary)
     fetch(requestUrl)
       .then((responseData) => responseData.text())
       .then((data) => {
         if (data)
           listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms)
+            Search.makeSearchSummary(data, searchTerms, anchor)
           );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
       });
   Search.output.appendChild(listItem);
 };
@@ -108,27 +120,46 @@ const _finishSearch = (resultCount) => {
       "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
     );
   else
-    Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
+    Search.status.innerText = Documentation.ngettext(
+      "Search finished, found one page matching the search query.",
+      "Search finished, found ${resultCount} pages matching the search query.",
+      resultCount,
+    ).replace('${resultCount}', resultCount);
 };
 const _displayNextItem = (
   results,
   resultCount,
-  searchTerms
+  searchTerms,
+  highlightTerms,
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), searchTerms);
+    _displayItem(results.pop(), searchTerms, highlightTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms),
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
       5
     );
   }
   // search finished, update title and status message
   else _finishSearch(resultCount);
 };
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
 
 /**
  * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
@@ -152,13 +183,26 @@ const Search = {
   _queued_query: null,
   _pulse_status: -1,
 
-  htmlToText: (htmlString) => {
+  htmlToText: (htmlString, anchor) => {
     const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    for (const removalQuery of [".headerlink", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
     const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
+    if (docContent) return docContent.textContent;
+
     console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
     );
     return "";
   },
@@ -211,6 +255,7 @@ const Search = {
     searchSummary.classList.add("search-summary");
     searchSummary.innerText = "";
     const searchList = document.createElement("ul");
+    searchList.setAttribute("role", "list");
     searchList.classList.add("search");
 
     const out = document.getElementById("search-results");
@@ -231,16 +276,7 @@ const Search = {
     else Search.deferQuery(query);
   },
 
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query: (query) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-    const allTitles = Search._index.alltitles;
-    const indexEntries = Search._index.indexentries;
-
+  _parseQuery: (query) => {
     // stem the search terms and add them to the correct list
     const stemmer = new Stemmer();
     const searchTerms = new Set();
@@ -276,22 +312,40 @@ const Search = {
     // console.info("required: ", [...searchTerms]);
     // console.info("excluded: ", [...excludedTerms]);
 
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename, kind].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
     _removeChildren(document.getElementById("search-progress"));
 
-    const queryLower = query.toLowerCase();
+    const queryLower = query.toLowerCase().trim();
     for (const [title, foundTitles] of Object.entries(allTitles)) {
-      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
         for (const [file, id] of foundTitles) {
-          let score = Math.round(100 * queryLower.length / title.length)
-          results.push([
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          normalResults.push([
             docNames[file],
             titles[file] !== title ? `${titles[file]} > ${title}` : title,
             id !== null ? "#" + id : "",
             null,
-            score,
+            score + boost,
             filenames[file],
+            SearchResultKind.title,
           ]);
         }
       }
@@ -300,46 +354,48 @@ const Search = {
     // search for explicit entries in index directives
     for (const [entry, foundEntries] of Object.entries(indexEntries)) {
       if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
-        for (const [file, id] of foundEntries) {
-          let score = Math.round(100 * queryLower.length / entry.length)
-          results.push([
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
             docNames[file],
             titles[file],
             id ? "#" + id : "",
             null,
             score,
             filenames[file],
-          ]);
+            SearchResultKind.index,
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
         }
       }
     }
 
     // lookup as object
     objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
     );
 
     // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
-        // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-      }
-      return leftScore > rightScore ? 1 : -1;
-    });
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
 
     // remove duplicate search results
     // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
@@ -353,14 +409,19 @@ const Search = {
       return acc;
     }, []);
 
-    results = results.reverse();
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
 
     // for debugging
     //Search.lastresults = results.slice();  // a copy
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, searchTerms);
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
   },
 
   /**
@@ -424,6 +485,7 @@ const Search = {
         descr,
         score,
         filenames[match[0]],
+        SearchResultKind.object,
       ]);
     };
     Object.keys(objects).forEach((prefix) =>
@@ -458,14 +520,18 @@ const Search = {
       // add support for partial matches
       if (word.length > 2) {
         const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
       }
 
       // no match but word was a required one
@@ -488,9 +554,8 @@ const Search = {
 
       // create the mapping
       files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
       });
     });
 
@@ -531,6 +596,7 @@ const Search = {
         null,
         score,
         filenames[file],
+        SearchResultKind.text,
       ]);
     }
     return results;
@@ -541,8 +607,8 @@ const Search = {
    * search summary for a given text. keywords is a list
    * of stemmed words.
    */
-  makeSearchSummary: (htmlText, keywords) => {
-    const text = Search.htmlToText(htmlText);
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
     if (text === "") return null;
 
     const textLower = text.toLowerCase();
diff --git a/latest/_static/sphinx-design.min.css b/latest/_static/sphinx-design.min.css
new file mode 100644
index 0000000000..860c36da0f
--- /dev/null
+++ b/latest/_static/sphinx-design.min.css
@@ -0,0 +1 @@
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!important}a.sd-bg-success:focus,a.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}.sd-bg-info{background-color:var(--sd-color-info) !important}.sd-bg-text-info{color:var(--sd-color-info-text) !important}button.sd-bg-info:focus,button.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}a.sd-bg-info:focus,a.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}.sd-bg-warning{background-color:var(--sd-color-warning) !important}.sd-bg-text-warning{color:var(--sd-color-warning-text) !important}button.sd-bg-warning:focus,button.sd-bg-warning:hover{background-color:var(--sd-color-warning-highlight) !important}a.sd-bg-warning:focus,a.sd-bg-warning:hover{background-color:var(--sd-color-warning-highlight) !important}.sd-bg-danger{background-color:var(--sd-color-danger) !important}.sd-bg-text-danger{color:var(--sd-color-danger-text) 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summary.sd-summary-title:focus{outline:none}details.sd-dropdown summary.sd-summary-title .sd-summary-icon{margin-right:.6em;display:inline-flex;align-items:center}details.sd-dropdown summary.sd-summary-title .sd-summary-icon svg{opacity:.8}details.sd-dropdown summary.sd-summary-title .sd-summary-text{flex-grow:1;line-height:1.5;padding-right:.5rem}details.sd-dropdown summary.sd-summary-title .sd-summary-state-marker{pointer-events:none;display:inline-flex;align-items:center}details.sd-dropdown summary.sd-summary-title .sd-summary-state-marker svg{opacity:.6}details.sd-dropdown summary.sd-summary-title:hover .sd-summary-state-marker svg{opacity:1;transform:scale(1.1)}details.sd-dropdown[open] summary .sd-octicon.no-title{visibility:hidden}details.sd-dropdown .sd-summary-chevron-right{transition:.25s}details.sd-dropdown[open]>.sd-summary-title .sd-summary-chevron-right{transform:rotate(90deg)}details.sd-dropdown[open]>.sd-summary-title 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0;position:relative}.sd-tab-set>input{opacity:0;position:absolute}.sd-tab-set>input:checked+label{border-color:var(--sd-color-tabs-underline-active);color:var(--sd-color-tabs-label-active)}.sd-tab-set>input:checked+label+.sd-tab-content{display:block}.sd-tab-set>input:not(:checked)+label:hover{color:var(--sd-color-tabs-label-hover);border-color:var(--sd-color-tabs-underline-hover)}.sd-tab-set>input:focus+label{outline-style:auto}.sd-tab-set>input:not(.focus-visible)+label{outline:none;-webkit-tap-highlight-color:transparent}.sd-tab-set>label{border-bottom:.125rem solid transparent;margin-bottom:0;color:var(--sd-color-tabs-label-inactive);border-color:var(--sd-color-tabs-underline-inactive);cursor:pointer;font-size:var(--sd-fontsize-tabs-label);font-weight:700;padding:1em 1.25em .5em;transition:color 250ms;width:auto;z-index:1}html .sd-tab-set>label:hover{color:var(--sd-color-tabs-label-active)}.sd-col>.sd-tab-set{width:100%}.sd-tab-content{box-shadow:0 -0.0625rem var(--sd-color-tabs-overline),0 .0625rem var(--sd-color-tabs-underline);display:none;order:99;padding-bottom:.75rem;padding-top:.75rem;width:100%}.sd-tab-content>:first-child{margin-top:0 !important}.sd-tab-content>:last-child{margin-bottom:0 !important}.sd-tab-content>.sd-tab-set{margin:0}.sd-sphinx-override,.sd-sphinx-override *{-moz-box-sizing:border-box;-webkit-box-sizing:border-box;box-sizing:border-box}.sd-sphinx-override p{margin-top:0}:root{--sd-color-primary: #0071bc;--sd-color-secondary: #6c757d;--sd-color-success: #28a745;--sd-color-info: #17a2b8;--sd-color-warning: #f0b37e;--sd-color-danger: #dc3545;--sd-color-light: #f8f9fa;--sd-color-muted: #6c757d;--sd-color-dark: #212529;--sd-color-black: black;--sd-color-white: white;--sd-color-primary-highlight: #0060a0;--sd-color-secondary-highlight: #5c636a;--sd-color-success-highlight: #228e3b;--sd-color-info-highlight: #148a9c;--sd-color-warning-highlight: #cc986b;--sd-color-danger-highlight: #bb2d3b;--sd-color-light-highlight: #d3d4d5;--sd-color-muted-highlight: #5c636a;--sd-color-dark-highlight: #1c1f23;--sd-color-black-highlight: black;--sd-color-white-highlight: #d9d9d9;--sd-color-primary-bg: rgba(0, 113, 188, 0.2);--sd-color-secondary-bg: rgba(108, 117, 125, 0.2);--sd-color-success-bg: rgba(40, 167, 69, 0.2);--sd-color-info-bg: rgba(23, 162, 184, 0.2);--sd-color-warning-bg: rgba(240, 179, 126, 0.2);--sd-color-danger-bg: rgba(220, 53, 69, 0.2);--sd-color-light-bg: rgba(248, 249, 250, 0.2);--sd-color-muted-bg: rgba(108, 117, 125, 0.2);--sd-color-dark-bg: rgba(33, 37, 41, 0.2);--sd-color-black-bg: rgba(0, 0, 0, 0.2);--sd-color-white-bg: rgba(255, 255, 255, 0.2);--sd-color-primary-text: #fff;--sd-color-secondary-text: #fff;--sd-color-success-text: #fff;--sd-color-info-text: #fff;--sd-color-warning-text: #212529;--sd-color-danger-text: #fff;--sd-color-light-text: #212529;--sd-color-muted-text: #fff;--sd-color-dark-text: #fff;--sd-color-black-text: #fff;--sd-color-white-text: #212529;--sd-color-shadow: rgba(0, 0, 0, 0.15);--sd-color-card-border: rgba(0, 0, 0, 0.125);--sd-color-card-border-hover: hsla(231, 99%, 66%, 1);--sd-color-card-background: transparent;--sd-color-card-text: inherit;--sd-color-card-header: transparent;--sd-color-card-footer: transparent;--sd-color-tabs-label-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-hover: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-inactive: hsl(0, 0%, 66%);--sd-color-tabs-underline-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-underline-hover: rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem;--sd-fontsize-dropdown: inherit;--sd-fontsize-dropdown-title: 1rem;--sd-fontweight-dropdown-title: 700}
diff --git a/latest/_static/sphinx_highlight.js b/latest/_static/sphinx_highlight.js
index aae669d7ea..8a96c69a19 100644
--- a/latest/_static/sphinx_highlight.js
+++ b/latest/_static/sphinx_highlight.js
@@ -29,14 +29,19 @@ const _highlight = (node, addItems, text, className) => {
       }
 
       span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
       parent.insertBefore(
         span,
         parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
+          rest,
           node.nextSibling
         )
       );
       node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
 
       if (isInSVG) {
         const rect = document.createElementNS(
@@ -140,5 +145,10 @@ const SphinxHighlight = {
   },
 };
 
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/latest/_static/version_switcher.js b/latest/_static/version_switcher.js
new file mode 100644
index 0000000000..162851d217
--- /dev/null
+++ b/latest/_static/version_switcher.js
@@ -0,0 +1,31 @@
+function load_versions(json){
+    var button = document.getElementById('version-switcher-button')
+    var container = document.getElementById('version-switcher-dropdown')
+    var loc = window.location.href;
+    var s = document.createElement('select');
+    s.style = "border-radius:5px;"
+    const versions = JSON.parse(json);
+    for (entry of versions){
+        var o = document.createElement('option');
+        var optionText = '';
+        if ('name' in entry){
+            optionText = entry.name;
+        }else{
+            optionText = entry.version;
+        }
+        o.value = entry.url;
+        if (current_version == entry.version){
+            o.selected = true;
+        }
+        o.innerHTML = optionText;
+        s.append(o);
+    }
+    s.addEventListener("change", ()=> {
+        var current_url = new URL(window.location.href);
+        var path = current_url.pathname;
+        //strip version from path
+        var page_path = path.substring(project_name.length+current_version.length+3);
+        window.location.href = s.value + page_path;
+    });
+    container.append(s);
+}
diff --git a/latest/algorithms.html b/latest/algorithms.html
index 8002f3435d..0a562f4680 100644
--- a/latest/algorithms.html
+++ b/latest/algorithms.html
@@ -1,30 +1,38 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
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-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Supported Algorithms &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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-  <![endif]-->
+  <title>Supported Algorithms &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
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+      <script src="_static/version_switcher.js?v=107152b5"></script>
+      <script src="_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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+    <link rel="prev" title="Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*" href="kaggle/automl.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -51,10 +59,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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@@ -63,73 +67,330 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
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+    <div id="version-switcher-button"
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+      data-bs-toggle="dropdown"
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+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Supported Algorithms</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#on-cpu">on CPU</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#classification">Classification</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#regression">Regression</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#clustering">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#dimensionality-reduction">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#dimensionality-reduction">Dimensionality Reduction</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#nearest-neighbors">Nearest Neighbors</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#other-tasks">Other tasks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#other-tasks">Other Tasks</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="#on-gpu">on GPU</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#id1">Classification</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#id2">Regression</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#id3">Clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#id4">Dimensionality reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id4">Dimensionality Reduction</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#scikit-learn-tests">Scikit-learn Tests</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="#scikit-learn-tests">Scikit-learn tests</a></li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -155,17 +416,17 @@
            <div itemprop="articleBody">
              
   <section id="supported-algorithms">
-<span id="sklearn-algorithms"></span><h1>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Permalink to this heading"></a></h1>
+<span id="sklearn-algorithms"></span><h1>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Link to this heading"></a></h1>
 <p>Applying Intel® Extension for Scikit-learn* impacts the following scikit-learn algorithms:</p>
 <section id="on-cpu">
-<h2>on CPU<a class="headerlink" href="#on-cpu" title="Permalink to this heading"></a></h2>
+<h2>on CPU<a class="headerlink" href="#on-cpu" title="Link to this heading"></a></h2>
 <section id="classification">
-<h3>Classification<a class="headerlink" href="#classification" title="Permalink to this heading"></a></h3>
+<h3>Classification<a class="headerlink" href="#classification" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -186,7 +447,7 @@ <h3>Classification<a class="headerlink" href="#classification" title="Permalink
 <td><p>All parameters are supported except:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
 </ul>
 </td>
@@ -218,12 +479,12 @@ <h3>Classification<a class="headerlink" href="#classification" title="Permalink
 </table>
 </section>
 <section id="regression">
-<h3>Regression<a class="headerlink" href="#regression" title="Permalink to this heading"></a></h3>
+<h3>Regression<a class="headerlink" href="#regression" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -244,7 +505,7 @@ <h3>Regression<a class="headerlink" href="#regression" title="Permalink to this
 <td><p>All parameters are supported except:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
 </ul>
 </td>
@@ -265,7 +526,7 @@ <h3>Regression<a class="headerlink" href="#regression" title="Permalink to this
 <li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
 </ul>
 </td>
-<td><p>Only dense data is supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+<td><p>Only dense data is supported.</p></td>
 </tr>
 <tr class="row-odd"><td><p><cite>Ridge</cite></p></td>
 <td><p>All parameters are supported except:</p>
@@ -297,12 +558,12 @@ <h3>Regression<a class="headerlink" href="#regression" title="Permalink to this
 </table>
 </section>
 <section id="clustering">
-<h3>Clustering<a class="headerlink" href="#clustering" title="Permalink to this heading"></a></h3>
+<h3>Clustering<a class="headerlink" href="#clustering" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -333,12 +594,12 @@ <h3>Clustering<a class="headerlink" href="#clustering" title="Permalink to this
 </table>
 </section>
 <section id="dimensionality-reduction">
-<h3>Dimensionality reduction<a class="headerlink" href="#dimensionality-reduction" title="Permalink to this heading"></a></h3>
+<h3>Dimensionality Reduction<a class="headerlink" href="#dimensionality-reduction" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -350,12 +611,16 @@ <h3>Dimensionality reduction<a class="headerlink" href="#dimensionality-reductio
 <tr class="row-even"><td><p><cite>PCA</cite></p></td>
 <td><p>All parameters are supported except:</p>
 <ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> != <cite>‘full’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
 </ul>
 </td>
 <td><p>Sparse data is not supported</p></td>
 </tr>
-<tr class="row-odd"><td><p><cite>TSNE</cite></p></td>
+<tr class="row-odd"><td><p><cite>IncrementalPCA</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>TSNE</cite></p></td>
 <td><p>All parameters are supported except:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != ‘euclidean’ or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
@@ -368,12 +633,12 @@ <h3>Dimensionality reduction<a class="headerlink" href="#dimensionality-reductio
 </table>
 </section>
 <section id="nearest-neighbors">
-<h3>Nearest Neighbors<a class="headerlink" href="#nearest-neighbors" title="Permalink to this heading"></a></h3>
+<h3>Nearest Neighbors<a class="headerlink" href="#nearest-neighbors" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -398,12 +663,12 @@ <h3>Nearest Neighbors<a class="headerlink" href="#nearest-neighbors" title="Perm
 </table>
 </section>
 <section id="other-tasks">
-<h3>Other tasks<a class="headerlink" href="#other-tasks" title="Permalink to this heading"></a></h3>
+<h3>Other Tasks<a class="headerlink" href="#other-tasks" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -412,15 +677,19 @@ <h3>Other tasks<a class="headerlink" href="#other-tasks" title="Permalink to thi
 </tr>
 </thead>
 <tbody>
-<tr class="row-even"><td><p><cite>train_test_split</cite></p></td>
+<tr class="row-even"><td><p><cite>EmpiricalCovariance</cite></p></td>
 <td><p>All parameters are supported</p></td>
 <td><p>Only dense data is supported</p></td>
 </tr>
-<tr class="row-odd"><td><p><cite>assert_all_finite</cite></p></td>
+<tr class="row-odd"><td><p><cite>train_test_split</cite></p></td>
 <td><p>All parameters are supported</p></td>
 <td><p>Only dense data is supported</p></td>
 </tr>
-<tr class="row-even"><td><p><cite>pairwise_distance</cite></p></td>
+<tr class="row-even"><td><p><cite>assert_all_finite</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>pairwise_distance</cite></p></td>
 <td><p>All parameters are supported except:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘cosine’</cite>, <cite>‘correlation’</cite>]</p></li>
@@ -428,7 +697,7 @@ <h3>Other tasks<a class="headerlink" href="#other-tasks" title="Permalink to thi
 </td>
 <td><p>Only dense data is supported</p></td>
 </tr>
-<tr class="row-odd"><td><p><cite>roc_auc_score</cite></p></td>
+<tr class="row-even"><td><p><cite>roc_auc_score</cite></p></td>
 <td><p>All parameters are supported except:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">average</span></code> != <cite>None</cite></p></li>
@@ -444,18 +713,18 @@ <h3>Other tasks<a class="headerlink" href="#other-tasks" title="Permalink to thi
 </section>
 </section>
 <section id="on-gpu">
-<h2>on GPU<a class="headerlink" href="#on-gpu" title="Permalink to this heading"></a></h2>
+<h2>on GPU<a class="headerlink" href="#on-gpu" title="Link to this heading"></a></h2>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p><a class="reference internal" href="oneapi-gpu.html#oneapi-gpu"><span class="std std-ref">oneAPI and GPU support in Intel® Extension for Scikit-learn*</span></a></p>
 </div>
 <section id="id1">
-<h3>Classification<a class="headerlink" href="#id1" title="Permalink to this heading"></a></h3>
+<h3>Classification<a class="headerlink" href="#id1" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -477,7 +746,7 @@ <h3>Classification<a class="headerlink" href="#id1" title="Permalink to this hea
 <td><p>All parameters are supported except:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
@@ -510,12 +779,12 @@ <h3>Classification<a class="headerlink" href="#id1" title="Permalink to this hea
 </table>
 </section>
 <section id="id2">
-<h3>Regression<a class="headerlink" href="#id2" title="Permalink to this heading"></a></h3>
+<h3>Regression<a class="headerlink" href="#id2" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -528,7 +797,7 @@ <h3>Regression<a class="headerlink" href="#id2" title="Permalink to this heading
 <td><p>All parameters are supported except:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
@@ -553,18 +822,18 @@ <h3>Regression<a class="headerlink" href="#id2" title="Permalink to this heading
 <li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
 </ul>
 </td>
-<td><p>Only dense data is supported, <cite>#observations</cite> should be &gt;= <cite>#features</cite>.</p></td>
+<td><p>Only dense data is supported.</p></td>
 </tr>
 </tbody>
 </table>
 </section>
 <section id="id3">
-<h3>Clustering<a class="headerlink" href="#id3" title="Permalink to this heading"></a></h3>
+<h3>Clustering<a class="headerlink" href="#id3" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -578,8 +847,8 @@ <h3>Clustering<a class="headerlink" href="#id3" title="Permalink to this heading
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">Init</span></code> = <cite>‘k-means++’</cite> fallbacks to CPU.</p></li>
 </ul>
-<p><code class="docutils literal notranslate"><span class="pre">Init</span></code> = <cite>‘k-means++’</cite> fallbacks to CPU.</p>
 </td>
 <td><p>Sparse data is not supported</p></td>
 </tr>
@@ -596,12 +865,12 @@ <h3>Clustering<a class="headerlink" href="#id3" title="Permalink to this heading
 </table>
 </section>
 <section id="id4">
-<h3>Dimensionality reduction<a class="headerlink" href="#id4" title="Permalink to this heading"></a></h3>
+<h3>Dimensionality Reduction<a class="headerlink" href="#id4" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -613,7 +882,7 @@ <h3>Dimensionality reduction<a class="headerlink" href="#id4" title="Permalink t
 <tr class="row-even"><td><p><cite>PCA</cite></p></td>
 <td><p>All parameters are supported except:</p>
 <ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> != <cite>‘full’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
 </ul>
 </td>
 <td><p>Sparse data is not supported</p></td>
@@ -622,12 +891,12 @@ <h3>Dimensionality reduction<a class="headerlink" href="#id4" title="Permalink t
 </table>
 </section>
 <section id="id5">
-<h3>Nearest Neighbors<a class="headerlink" href="#id5" title="Permalink to this heading"></a></h3>
+<h3>Nearest Neighbors<a class="headerlink" href="#id5" title="Link to this heading"></a></h3>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 17%" />
-<col style="width: 50%" />
-<col style="width: 33%" />
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Algorithm</p></th>
@@ -649,12 +918,255 @@ <h3>Nearest Neighbors<a class="headerlink" href="#id5" title="Permalink to this
 </tbody>
 </table>
 </section>
+<section id="id6">
+<h3>Other Tasks<a class="headerlink" href="#id6" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>EmpiricalCovariance</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="spmd-support">
+<h2>SPMD Support<a class="headerlink" href="#spmd-support" title="Link to this heading"></a></h2>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="distributed-mode.html#distributed"><span class="std std-ref">Distributed Mode</span></a></p>
+</div>
+<section id="id7">
+<h3>Classification<a class="headerlink" href="#id7" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>RandomForestClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘gini’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsClassifier</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> method not supported</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LogisticRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">solver</span></code> != <cite>‘newton-cg’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">class_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">penalty</span></code> != <cite>‘l2’</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id8">
+<h3>Regression<a class="headerlink" href="#id8" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>RandomForestRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">warm_start</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ccp_alpha</span></code> != <cite>0</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">criterion</span></code> != <cite>‘mse’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">oob_score</span></code> = <cite>True</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Multi-output and sparse data are not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>KNeighborsRegressor</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite> or <cite>‘minkowski’</cite> with <code class="docutils literal notranslate"><span class="pre">p</span></code> != <cite>2</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+<tr class="row-even"><td><p><cite>LinearRegression</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">normalize</span></code> != <cite>False</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id9">
+<h3>Clustering<a class="headerlink" href="#id9" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>KMeans</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">precompute_distances</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> != <cite>None</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">Init</span></code> = <cite>‘k-means++’</cite> fallbacks to CPU.</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+<tr class="row-odd"><td><p><cite>DBSCAN</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> != <cite>‘euclidean’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> not in [<cite>‘brute’</cite>, <cite>‘auto’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id10">
+<h3>Dimensionality Reduction<a class="headerlink" href="#id10" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters &amp; Methods</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>PCA</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">svd_solver</span></code> not in [<cite>‘full’</cite>, <cite>‘covariance_eigh’</cite>]</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fit</span></code> is the only method supported</p></li>
+</ul>
+</td>
+<td><p>Sparse data is not supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id11">
+<h3>Nearest Neighbors<a class="headerlink" href="#id11" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>NearestNeighbors</cite></p></td>
+<td><p>All parameters are supported except:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> != <cite>‘brute’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">weights</span></code> = <cite>‘callable’</cite></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">metric</span></code> not in [<cite>‘euclidean’</cite>, <cite>‘manhattan’</cite>, <cite>‘minkowski’</cite>, <cite>‘chebyshev’</cite>, <cite>‘cosine’</cite>]</p></li>
+</ul>
+</td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="id12">
+<h3>Other Tasks<a class="headerlink" href="#id12" title="Link to this heading"></a></h3>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 16.7%" />
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Parameters</p></th>
+<th class="head"><p>Data formats</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><cite>EmpiricalCovariance</cite></p></td>
+<td><p>All parameters are supported</p></td>
+<td><p>Only dense data is supported</p></td>
+</tr>
+</tbody>
+</table>
+</section>
 </section>
 <section id="scikit-learn-tests">
-<h2>Scikit-learn tests<a class="headerlink" href="#scikit-learn-tests" title="Permalink to this heading"></a></h2>
+<h2>Scikit-learn Tests<a class="headerlink" href="#scikit-learn-tests" title="Link to this heading"></a></h2>
 <p>Monkey-patched scikit-learn classes and functions passes scikit-learn’s own test
-suite, with few exceptions, specified in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/deselected_tests.yaml">deselected_tests.yaml</a>.</p>
-<p>The results of the entire latest scikit-learn test suite with Intel® Extension for Scikit-learn*: <a class="reference external" href="https://circleci.com/gh/intel/scikit-learn-intelex">CircleCI</a>.</p>
+suite, with few exceptions, specified in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/deselected_tests.yaml">deselected_tests.yaml</a>.</p>
 </section>
 </section>
 
@@ -662,14 +1174,14 @@ <h2>Scikit-learn tests<a class="headerlink" href="#scikit-learn-tests" title="Pe
            </div>
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diff --git a/latest/array_api.html b/latest/array_api.html
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@@ -0,0 +1,720 @@
+
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
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+<body class="wy-body-for-nav"> 
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+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+  <div class="version-switcher__container dropdown">
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+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+      Choose version  <!-- this text may get changed later by javascript -->
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+             
+  <section id="array-api-support">
+<span id="array-api"></span><h1>Array API support<a class="headerlink" href="#array-api-support" title="Link to this heading"></a></h1>
+<p>The <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API</a> specification defines
+a standard API for all array manipulation libraries with a NumPy-like API.
+Intel(R) Extension for Scikit-Learn doesn’t require
+<a class="reference external" href="https://github.com/data-apis/array-api-compat">array-api-compat</a> to be installed for
+functional support of the array API standard.
+In the current implementation, the functional support of array api follows the functional
+support of different array or DataFrame inputs and does not modify the precision of the
+input and output data formats unless necessary. Any array API input will be converted to host
+numpy.ndarrays and all internal manipulations with data will be done with these representations of
+the input data. DPNP’s ‘ndarray’ and Data Parallel Control’s ‘usm_ndarray’ have special handling
+requirements that are described in the relevant section of this document. Output values will in
+all relevant cases match the input data format.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Currently, only <a class="reference external" href="https://github.com/data-apis/array-api-strict">array-api-strict</a>,
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a>, <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp</a>
+and <a class="reference external" href="https://numpy.org/">numpy</a> are known to work with sklearnex estimators.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Stock Scikit-learn’s array API support requires <a class="reference external" href="https://github.com/data-apis/array-api-compat">array-api-compat</a> to be installed.</p>
+</div>
+<section id="support-for-dpnp-and-dpctl">
+<h2>Support for DPNP and DPCTL<a class="headerlink" href="#support-for-dpnp-and-dpctl" title="Link to this heading"></a></h2>
+<p>The functional support of input data for sklearnex estimators also extended for SYCL USM array types.
+These include SYCL USM arrays <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp’s</a> ndarray and
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">Data Parallel Control usm_ndarray</a>.
+DPNP ndarray and Data Parallel Control usm_ndarray contain SYCL contexts which can be used for
+<cite>sklearnex</cite> device offloading.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Current support for DPNP and DPCTL usm_ndarray data can be copied and moved to and from device in sklearnex and have
+impacts on memory utilization.</p>
+</div>
+<p>DPCTL or DPNP inputs are not required to use <cite>config_context(target_offload=device)</cite>.
+<cite>sklearnex</cite> will use input usm_ndarray sycl context for device offloading.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>As DPCTL or DPNP inputs contain SYCL contexts, they do not require <cite>config_context(target_offload=device)</cite>.
+However, the use of <cite>config_context`</cite> will override the contained SYCL context and will force movement
+of data to the targeted device.</p>
+</div>
+</section>
+<section id="support-for-array-api-compatible-inputs">
+<h2>Support for Array API-compatible inputs<a class="headerlink" href="#support-for-array-api-compatible-inputs" title="Link to this heading"></a></h2>
+<p>All patched estimators, metrics, tools and non-scikit-learn estimators functionally support Array API standard.
+Intel(R) Extension for scikit-Learn preserves input data format for all outputs. For all array inputs except
+SYCL USM arrays <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp’s</a> ndarray and
+<a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">Data Parallel Control usm_ndarray</a> all computation
+will be only accomplished on CPU unless specified by a <cite>config_context`</cite> with an available GPU device.</p>
+<p>Stock scikit-learn uses <cite>config_context(array_api_dispatch=True)</cite> for enabling Array API
+<a class="reference external" href="https://scikit-learn.org/1.5/modules/array_api.html">support</a>.
+If <cite>array_api_dispatch</cite> is enabled and the installed Scikit-Learn version supports array API, then the original
+inputs are used when falling back to Scikit-Learn functionality.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Data Parallel Control usm_ndarray or DPNP ndarray inputs will use host numpy data copies when
+falling back to Scikit-Learn since they are not array API compliant.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Functional support doesn’t guarantee that after the model is trained, fitted attributes that are arrays
+will also be from the same namespace as the training data.</p>
+</div>
+</section>
+<section id="example-usage">
+<h2>Example usage<a class="headerlink" href="#example-usage" title="Link to this heading"></a></h2>
+<section id="dpnp-ndarrays">
+<h3>DPNP ndarrays<a class="headerlink" href="#dpnp-ndarrays" title="Link to this heading"></a></h3>
+<p>Here is an example code to demonstrate how to use <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp</a> arrays to
+run <cite>RandomForestRegressor</cite> on a GPU without <cite>config_context(array_api_dispatch=True)</cite>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2023 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="c1"># sklearnex RF example for GPU offloading with DPNP ndarray:</span>
+<span class="c1">#    python ./random_forest_regressor_dpnp_batch.py</span>
+
+<span class="kn">import</span> <span class="nn">dpnp</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+
+<span class="kn">from</span> <span class="nn">sklearnex.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
+
+<span class="n">sycl_device</span> <span class="o">=</span> <span class="s2">&quot;gpu:0&quot;</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="n">dpnp_X_train</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">sycl_device</span><span class="p">)</span>
+<span class="n">dpnp_y_train</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">sycl_device</span><span class="p">)</span>
+<span class="n">dpnp_X_test</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">sycl_device</span><span class="p">)</span>
+
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dpnp_X_train</span><span class="p">,</span> <span class="n">dpnp_y_train</span><span class="p">)</span>
+
+<span class="n">pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">dpnp_X_test</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Random Forest regression results:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Ground truth (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">y_test</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Regression results (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Are predicted results on GPU: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">sycl_device</span><span class="o">.</span><span class="n">is_gpu</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Functional support doesn’t guarantee that after the model is trained, fitted attributes that are arrays
+will also be from the same namespace as the training data.</p>
+</div>
+<p>For example, if <a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp’s</a> namespace was used for training,
+then fitted attributes will be on the CPU and <cite>numpy.ndarray</cite> data format.</p>
+</section>
+<section id="dpctl-usm-ndarrays">
+<h3>DPCTL usm_ndarrays<a class="headerlink" href="#dpctl-usm-ndarrays" title="Link to this heading"></a></h3>
+<p>Here is an example code to demonstrate how to use <a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a>
+arrays to run <cite>RandomForestClassifier</cite> on a GPU witout <cite>config_context(array_api_dispatch=True)</cite>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2023 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="c1"># sklearnex RF example for GPU offloading with DPCtl tensor:</span>
+<span class="c1">#    python ./random_forest_classifier_dpctl_batch.py</span>
+
+<span class="kn">import</span> <span class="nn">dpctl</span>
+<span class="kn">import</span> <span class="nn">dpctl.tensor</span> <span class="k">as</span> <span class="nn">dpt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+
+<span class="kn">from</span> <span class="nn">sklearnex.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
+
+<span class="c1"># Make sure that all DPCtl tensors using the same device.</span>
+<span class="n">q</span> <span class="o">=</span> <span class="n">dpctl</span><span class="o">.</span><span class="n">SyclQueue</span><span class="p">(</span><span class="s2">&quot;gpu&quot;</span><span class="p">)</span>  <span class="c1"># GPU</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+    <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="n">dpt_X_train</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">usm_type</span><span class="o">=</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">sycl_queue</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+<span class="n">dpt_y_train</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">usm_type</span><span class="o">=</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">sycl_queue</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+<span class="n">dpt_X_test</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">usm_type</span><span class="o">=</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">sycl_queue</span><span class="o">=</span><span class="n">q</span><span class="p">)</span>
+
+<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dpt_X_train</span><span class="p">,</span> <span class="n">dpt_y_train</span><span class="p">)</span>
+
+<span class="n">pred</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">dpt_X_test</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Random Forest classification results:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Ground truth (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">y_test</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Classification results (first 5 observations):</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="p">[:</span><span class="mi">5</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Are predicted results on GPU: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">sycl_device</span><span class="o">.</span><span class="n">is_gpu</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>As on previous example, if <a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> Array API namespace was
+used for training, then fitted attributes will be on the CPU and <cite>numpy.ndarray</cite> data format.</p>
+</section>
+<section id="use-of-array-api-strict">
+<h3>Use of <cite>array-api-strict</cite><a class="headerlink" href="#use-of-array-api-strict" title="Link to this heading"></a></h3>
+<p>Here is an example code to demonstrate how to use <a class="reference external" href="https://github.com/data-apis/array-api-strict">array-api-strict</a>
+arrays to run <cite>DBSCAN</cite>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ==============================================================================</span>
+<span class="c1"># Copyright 2024 Intel Corporation</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1"># ==============================================================================</span>
+
+<span class="kn">import</span> <span class="nn">array_api_strict</span>
+
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">config_context</span><span class="p">,</span> <span class="n">patch_sklearn</span>
+
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">array_api_strict</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
+    <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.0</span><span class="p">,</span> <span class="mf">80.0</span><span class="p">]],</span>
+    <span class="n">dtype</span><span class="o">=</span><span class="n">array_api_strict</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Could be launched without `config_context(array_api_dispatch=True)`. This context</span>
+<span class="c1"># manager for sklearnex, only guarantee that in case of the fallback to stock</span>
+<span class="c1"># scikit-learn, fitted attributes to be from the same Array API namespace as</span>
+<span class="c1"># the training data.</span>
+<span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Fitted labels :</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">clustering</span><span class="o">.</span><span class="n">labels_</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+</section>
+
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">How to Contribute</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#issues">Issues</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#pull-requests">Pull Requests</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#before-contributing-changes">Before Contributing Changes</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="#code-style">Code Style</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -139,40 +416,41 @@
            <div itemprop="articleBody">
              
   <section id="how-to-contribute">
-<h1>How to Contribute<a class="headerlink" href="#how-to-contribute" title="Permalink to this heading"></a></h1>
-<p>We welcome community contributions to Intel® Extension for Scikit-learn*. You can:</p>
-<ul class="simple">
-<li><p>Submit your changes directly with a <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/pulls">pull request</a>.</p></li>
-<li><p>Log a bug or make a feature request with an <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">issue</a>.</p></li>
-</ul>
-<p>Refer to our guidelines on pull requests and issues before you proceed.</p>
-<section id="issues">
-<h2>Issues<a class="headerlink" href="#issues" title="Permalink to this heading"></a></h2>
-<p>Use <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">GitHub issues</a> to:</p>
-<ul class="simple">
-<li><p>report an issue</p></li>
-<li><p>make a feature request</p></li>
-</ul>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To report a vulnerability, refer to
-<a class="reference external" href="https://www.intel.com/content/www/us/en/security-center/default.html">Intel vulnerability reporting policy</a>.</p>
-</div>
+<h1>How to Contribute<a class="headerlink" href="#how-to-contribute" title="Link to this heading"></a></h1>
+<p>As an open source project, we welcome community contributions to Intel(R) Extension for Scikit-learn.
+This document explains how to participate in project conversations, log bugs and enhancement requests, and submit code patches.</p>
+<section id="licensing">
+<h2>Licensing<a class="headerlink" href="#licensing" title="Link to this heading"></a></h2>
+<p>Intel(R) Extension for Scikit-learn uses the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE">Apache 2.0 License</a>. By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms.</p>
 </section>
 <section id="pull-requests">
-<h2>Pull Requests<a class="headerlink" href="#pull-requests" title="Permalink to this heading"></a></h2>
-<p>To contribute your changes directly to the repository, do the following:</p>
-<ul class="simple">
-<li><p>Make sure you can build the product and run all the examples with your patch.</p></li>
-<li><p>For a larger feature, provide a relevant example.</p></li>
-<li><p>Document your code.</p></li>
-<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/pulls">Submit</a> a pull request into the <cite>master</cite> branch.
-Provide a brief description of the changes you are contributing.</p></li>
-</ul>
-<p>Public CI is enabled for the repository. Your PR should pass all of our checks.
-We will review your contribution and, if any additional fixes or modifications are necessary,
-we may give some feedback to guide you.
-When accepted, your pull request will be merged into our GitHub repository.</p>
+<h2>Pull Requests<a class="headerlink" href="#pull-requests" title="Link to this heading"></a></h2>
+<p>No anonymous contributions are accepted. The name in the commit message Signed-off-by line and your email must match the change authorship information.</p>
+<p>Make sure your .gitconfig is set up correctly so you can use git commit -s for signing your patches:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">name</span> <span class="s2">&quot;Kate Developer&quot;</span>
+<span class="n">git</span> <span class="n">config</span> <span class="o">--</span><span class="k">global</span> <span class="n">user</span><span class="o">.</span><span class="n">email</span> <span class="n">kate</span><span class="o">.</span><span class="n">developer</span><span class="nd">@company</span><span class="o">.</span><span class="n">com</span>
+</pre></div>
+</div>
+<section id="before-contributing-changes">
+<h3>Before Contributing Changes<a class="headerlink" href="#before-contributing-changes" title="Link to this heading"></a></h3>
+<p>Make sure you can build the product and run all the tests with your patch.
+For a larger feature, provide a relevant test.
+Document your code. Our project uses reStructuredText for documentation.
+For new file(s), specify the appropriate copyright year in the first line.
+Submit a pull request into the master branch.
+Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository.</p>
+</section>
+</section>
+<section id="code-style">
+<h2>Code Style<a class="headerlink" href="#code-style" title="Link to this heading"></a></h2>
+<p>We use <a class="reference external" href="https://black.readthedocs.io/en/stable/">black</a> and <a class="reference external" href="https://pycqa.github.io/isort/">isort</a> formatters for Python* code.
+The line length is 90 characters; use default options otherwise. You can find the linter configuration in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml">.pyproject.toml</a>.</p>
+<p>A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools.</p>
+<p>Optionally, you can install pre-commit hooks that do the formatting for you. For this, run from the top level of the repository:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pre</span><span class="o">-</span><span class="n">commit</span>
+<span class="n">pre</span><span class="o">-</span><span class="n">commit</span> <span class="n">install</span>
+</pre></div>
+</div>
 </section>
 </section>
 
@@ -180,14 +458,14 @@ <h2>Pull Requests<a class="headerlink" href="#pull-requests" title="Permalink to
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index e68e4dd887..277b7712cd 100644
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+++ b/latest/deprecation.html
@@ -1,30 +1,38 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
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     <script src="_static/js/theme.js"></script>
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-    <link rel="next" title="Installation" href="installation.html" />
-    <link rel="prev" title="Release Notes" href="releases.html" />
+    <link rel="next" title="Tuning Guide" href="guide/acceleration.html" />
+    <link rel="prev" title="Preview Functionality" href="preview.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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@@ -51,10 +59,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,56 +67,330 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
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+      aria-controls="version-switcher-dropdown"
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+    >
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+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Deprecation Notice</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#macos-support">macOS* Support</a></li>
 </ul>
 </li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
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@@ -138,10 +416,10 @@
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   <section id="deprecation-notice">
-<h1>Deprecation Notice<a class="headerlink" href="#deprecation-notice" title="Permalink to this heading"></a></h1>
+<h1>Deprecation Notice<a class="headerlink" href="#deprecation-notice" title="Link to this heading"></a></h1>
 <p>This page provides information about the deprecations of a specific Intel® Extension for Scikit-learn* functionality.</p>
 <section id="macos-support">
-<h2>macOS* Support<a class="headerlink" href="#macos-support" title="Permalink to this heading"></a></h2>
+<h2>macOS* Support<a class="headerlink" href="#macos-support" title="Link to this heading"></a></h2>
 <p><strong>Deprecation:</strong> macOS* support is deprecated for Intel® Extension for Scikit-learn*. The 2023.x releases are the last to provide it.</p>
 <p><strong>Reasons for deprecation:</strong> No modern X86 macOS*-based systems are to be released.</p>
 <p><strong>Alternatives:</strong> The 2023.x version on macOS*.</p>
@@ -152,14 +430,14 @@ <h2>macOS* Support<a class="headerlink" href="#macos-support" title="Permalink t
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+  <div class="version-switcher__container dropdown">
+    <script>
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diff --git a/latest/distributed-mode.html b/latest/distributed-mode.html
index 07a26931e9..cb0bbfd09f 100644
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@@ -1,30 +1,38 @@
+
+
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           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
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@@ -63,53 +67,330 @@
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 </div>
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+  <div class="version-switcher__container dropdown">
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+      data-bs-toggle="dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul class="current">
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+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
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-<ul>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
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-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
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@@ -135,16 +416,31 @@
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   <section id="distributed-mode">
-<span id="distributed"></span><h1>Distributed Mode<a class="headerlink" href="#distributed-mode" title="Permalink to this heading"></a></h1>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Intel® Extension for Scikit-learn* contains scikit-learn patching functionality that was originally available in
-<a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/master/daal4py">daal4py</a> package.
-We recommend you to use scikit-learn-intelex package instead of daal4py.
-You can learn more about daal4py in <a class="reference external" href="https://intelpython.github.io/daal4py">daal4py documentation</a>.</p>
+<span id="distributed"></span><h1>Distributed Mode<a class="headerlink" href="#distributed-mode" title="Link to this heading"></a></h1>
+<p>Intel® Extension for Scikit-learn* offers Single Program, Multiple Data (SPMD) supported interfaces for distributed computing.
+Several <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/oneapi-gpu.html#">GPU-supported algorithms</a>
+also provide distributed, multi-GPU computing capabilities via integration with <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code>. The prerequisites
+match those of GPU computing, along with an MPI backend of your choice (<a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html#gs.dcan6r">Intel MPI recommended</a>, available
+via <code class="docutils literal notranslate"><span class="pre">impi-devel</span></code> python package) and the <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code> python package. If using Intel® Extension for Scikit-learn*
+<a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md#build-from-sources">installed from sources</a>,
+ensure that the spmd_backend is built.</p>
+<p>Note that Intel® Extension for Scikit-learn* now supports GPU offloading to speed up MPI operations. This is supported automatically with
+some MPI backends, but in order to use GPU offloading with Intel MPI, set the following environment variable (providing
+data on device without this may lead to a runtime error):</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">I_MPI_OFFLOAD</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+<p>Estimators can be imported from the <code class="docutils literal notranslate"><span class="pre">sklearnex.spmd</span></code> module. Data should be distributed across multiple nodes as
+desired, and should be transfered to a dpctl or dpnp array before being passed to the estimator. View a full
+example of this process in the Intel® Extension for Scikit-learn* repository, where many examples of our SPMD-supported estimators are
+available: <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/examples/sklearnex/">https://github.com/intel/scikit-learn-intelex/blob/main/examples/sklearnex/</a>. To run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mpirun</span> <span class="o">-</span><span class="n">n</span> <span class="mi">4</span> <span class="n">python</span> <span class="n">linear_regression_spmd</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
 </div>
-<p>While daal4py is available in <a class="reference external" href="https://intelpython.github.io/daal4py/scaling.html">distribued mode</a>,
-Intel® Extension for Scikit-learn* does not currently offer this functionality.</p>
+<p>Note that additional mpirun arguments can be added as desired. SPMD-supported estimators are listed in the
+<a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html#spmd-support">algorithms support documentation</a>.</p>
+<p>Additionally, daal4py offers some distributed functionality, see
+<a class="reference external" href="https://intelpython.github.io/daal4py/scaling.html">documentation</a> for further details.</p>
 </section>
 
 
@@ -152,13 +448,13 @@
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@@ -1,28 +1,35 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
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+  <title>Index &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
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+      <script src="_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
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@@ -49,10 +56,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -61,53 +64,330 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
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-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -135,32 +415,209 @@
 <h1 id="index">Index</h1>
 
 <div class="genindex-jumpbox">
- <a href="#G"><strong>G</strong></a>
+ <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
  | <a href="#P"><strong>P</strong></a>
- | <a href="#U"><strong>U</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#V"><strong>V</strong></a>
  
 </div>
-<h2 id="G">G</h2>
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics">BasicStatistics (class in sklearnex.basic_statistics)</a>
+</li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_">batch_size_ (sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_">(sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_">(sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_">coef_ (sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_">covariance_ (sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit">fit() (sklearnex.basic_statistics.BasicStatistics method)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit">(sklearnex.basic_statistics.IncrementalBasicStatistics method)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit">(sklearnex.covariance.IncrementalEmpiricalCovariance method)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit">(sklearnex.linear_model.IncrementalLinearRegression method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics">IncrementalBasicStatistics (class in sklearnex.basic_statistics)</a>
+</li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance">IncrementalEmpiricalCovariance (class in sklearnex.covariance)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression">IncrementalLinearRegression (class in sklearnex.linear_model)</a>
+</li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_">intercept_ (sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_">location_ (sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="what-is-patching.html#term-global-patching"><strong>global patching</strong></a>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_">max_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_">mean_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_">min_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_">n_features_in_ (sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_">(sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_">(sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_">n_samples_seen_ (sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_">(sklearnex.covariance.IncrementalEmpiricalCovariance attribute)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_">(sklearnex.linear_model.IncrementalLinearRegression attribute)</a>
 </li>
+      </ul></li>
   </ul></td>
 </tr></table>
 
 <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="what-is-patching.html#term-patching"><strong>patching</strong></a>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit">partial_fit() (sklearnex.basic_statistics.IncrementalBasicStatistics method)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit">(sklearnex.covariance.IncrementalEmpiricalCovariance method)</a>
+</li>
+        <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit">(sklearnex.linear_model.IncrementalLinearRegression method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict">predict() (sklearnex.linear_model.IncrementalLinearRegression method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_">second_order_raw_moment_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_">standard_deviation_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_">sum_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_">sum_squares_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_">sum_squares_centered_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
 </li>
+      </ul></li>
   </ul></td>
 </tr></table>
 
-<h2 id="U">U</h2>
+<h2 id="V">V</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="what-is-patching.html#term-unpatching"><strong>unpatching</strong></a>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_">variance_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_">variation_ (sklearnex.basic_statistics.BasicStatistics attribute)</a>
+
+      <ul>
+        <li><a href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_">(sklearnex.basic_statistics.IncrementalBasicStatistics attribute)</a>
 </li>
+      </ul></li>
   </ul></td>
 </tr></table>
 
@@ -173,7 +630,7 @@ <h2 id="U">U</h2>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0e5df40>
+<jinja2.runtime.BlockReference object at 0x7f04b6123fb0>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -198,6 +655,28 @@ <h2 id="U">U</h2>
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/latest/guide/acceleration.html b/latest/guide/acceleration.html
index 75fc5dff2c..68d921c7a6 100644
--- a/latest/guide/acceleration.html
+++ b/latest/guide/acceleration.html
@@ -1,30 +1,38 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Performance Tuning Guide &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/custom.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/tabs.css" type="text/css" />
-    <link rel="shortcut icon" href="../_static/favicons.png"/>
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Tuning Guide &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=132153fd" />
+      <link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
+
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="../_static/documentation_options.js?v=aa5d0537"></script>
+      <script src="../_static/doctools.js?v=9bcbadda"></script>
+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="Follow us on Medium" href="../blogs.html" />
-    <link rel="prev" title="Acceleration" href="../acceleration.html" />
+    <link rel="next" title="Intel® Extension for Scikit-learn* Tutorials &amp; Case Studies" href="../tutorials.html" />
+    <link rel="prev" title="Deprecation Notice" href="../deprecation.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -51,10 +59,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,57 +67,330 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
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+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
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 <li class="toctree-l2"><a class="reference internal" href="#tsne">TSNE</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#random-forest">Random Forest</a></li>
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-  <section id="performance-tuning-guide">
-<h1>Performance Tuning Guide<a class="headerlink" href="#performance-tuning-guide" title="Permalink to this heading"></a></h1>
+  <section id="tuning-guide">
+<h1>Tuning Guide<a class="headerlink" href="#tuning-guide" title="Link to this heading"></a></h1>
 <p>The performance of some algorithms changes based on the parameters that are used.
 This section denotes the details of such cases.</p>
 <p>Refer to <a class="reference internal" href="../algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a> to see the full list of algorithms, parameters, and data formats supported in Intel® Extension for Scikit-learn*.</p>
 <section id="tsne">
-<span id="acceleration-tsne"></span><h2>TSNE<a class="headerlink" href="#tsne" title="Permalink to this heading"></a></h2>
+<span id="acceleration-tsne"></span><h2>TSNE<a class="headerlink" href="#tsne" title="Link to this heading"></a></h2>
 <p>TSNE algorithm consists of two components: KNN and Gradient Descent.
 The overall acceleration of TSNE depends on the acceleration of each of these algorithms.</p>
 <ul class="simple">
@@ -164,17 +441,17 @@ <h1>Performance Tuning Guide<a class="headerlink" href="#performance-tuning-guid
 <p>To get better performance, use parameters supported by both components.</p>
 </section>
 <section id="random-forest">
-<span id="acceleration-rf"></span><h2>Random Forest<a class="headerlink" href="#random-forest" title="Permalink to this heading"></a></h2>
+<span id="acceleration-rf"></span><h2>Random Forest<a class="headerlink" href="#random-forest" title="Link to this heading"></a></h2>
 <p>Random Forest models accelerated with Intel® Extension for Scikit-learn* and using the <cite>hist</cite> splitting
 method discretize training data by creating a histogram with a configurable
 number of bins. The following keyword arguments can be used to influence the
 created histogram.</p>
 <table class="docutils align-left">
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+<col style="width: 50.0%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Keyword argument</p></th>
@@ -218,14 +495,14 @@ <h1>Performance Tuning Guide<a class="headerlink" href="#performance-tuning-guid
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@@ -50,10 +60,6 @@
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+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -134,108 +417,55 @@
            <div itemprop="articleBody">
              
   <section id="intelex">
-<span id="index"></span><h1>Intel® Extension for Scikit-learn*<a class="headerlink" href="#intelex" title="Permalink to this heading"></a></h1>
-<p>With Intel® Extension for Scikit-learn* you can accelerate your Scikit-learn applications and
-still have full conformance with all Scikit-Learn APIs and algorithms.
-Intel® Extension for Scikit-learn* is a free software AI accelerator that brings over 10-100X acceleration across a variety of applications.</p>
-<p>Intel® Extension for Scikit-learn* offers you a way to accelerate existing scikit-learn code.
-The acceleration is achieved through <a class="reference internal" href="what-is-patching.html#term-patching"><span class="xref std std-term">patching</span></a>:
-replacing the stock scikit-learn algorithms with their optimized versions provided by the extension.</p>
+<span id="index"></span><h1>Intel® Extension for Scikit-learn*<a class="headerlink" href="#intelex" title="Link to this heading"></a></h1>
+<p>Intel(R) Extension for Scikit-learn is a <strong>free software AI accelerator</strong> designed to deliver up to <strong>100X</strong> faster performance for your existing scikit-learn code.
+The software acceleration is achieved with vector instructions, AI hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel(R) platforms at launch time.</p>
 <p class="rubric">Designed for Data Scientists and Framework Designers</p>
-<p>Intel® Extension for Scikit-learn* was created to provide data scientists with a way to get a better performance
-while using the familiar scikit-learn package and getting the same results.</p>
-<section id="usage">
-<h2>Usage<a class="headerlink" href="#usage" title="Permalink to this heading"></a></h2>
-<p>You may enable patching in different ways:</p>
-<div class="admonition important">
-<p class="admonition-title">Important</p>
-<p>These patching methods are interchangeable.
-They support different enabling scenarios while producing the same result.</p>
-</div>
-<ul>
-<li><p>Without editing the code of a scikit-learn application by using the following command line flag:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span> <span class="n">my_application</span><span class="o">.</span><span class="n">py</span>
-</pre></div>
-</div>
-</li>
-<li><p>Directly from the script:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
-<span class="n">patch_sklearn</span><span class="p">()</span>
-</pre></div>
-</div>
-</li>
-</ul>
-<div class="admonition important">
-<p class="admonition-title">Important</p>
-<p>You have to import scikit-learn <strong>after</strong> these lines. Otherwise, the patching will not affect the original scikit-learn estimators.</p>
-</div>
-<ul>
-<li><p>Through importing the desired estimator from the sklearnex module in your script:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
-</pre></div>
-</div>
-</li>
-<li><p>Through <a class="reference internal" href="what-is-patching.html#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
-</pre></div>
-</div>
-</li>
+<p>Use Intel(R) Extension for Scikit-learn, to:</p>
+<ul class="simple">
+<li><p>Speed up training and inference by up to 100x with the equivalent mathematical accuracy</p></li>
+<li><p>Benefit from performance improvements across different x86-compatible CPUs or Intel(R) GPUs</p></li>
+<li><p>Integrate the extension into your existing Scikit-learn applications without code modifications</p></li>
+<li><p>Enable and disable the extension with a couple of lines of code or at the command line</p></li>
 </ul>
-<p class="rubric">Example</p>
+<p>Intel(R) Extension for Scikit-learn is also a part of <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html">Intel(R) AI Tools</a>.</p>
+<a class="reference internal image-reference" href="_images/scikit-learn-acceleration.PNG"><img alt="_images/scikit-learn-acceleration.PNG" src="_images/scikit-learn-acceleration.PNG" style="width: 800px;" />
+</a>
+<p>These performance charts use benchmarks that you can find in the <a class="reference external" href="https://github.com/IntelPython/scikit-learn_bench">scikit-learn bench repository</a>.</p>
+<section id="supported-algorithms">
+<h2>Supported Algorithms<a class="headerlink" href="#supported-algorithms" title="Link to this heading"></a></h2>
+<p>See all of the <a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported Algorithms</span></a>.</p>
+</section>
+<section id="intel-r-optimizations">
+<h2>Intel(R) Optimizations<a class="headerlink" href="#intel-r-optimizations" title="Link to this heading"></a></h2>
+<section id="enable-intel-r-cpu-optimizations">
+<h3>Enable Intel(R) CPU Optimizations<a class="headerlink" href="#enable-intel-r-cpu-optimizations" title="Link to this heading"></a></h3>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
 <span class="n">patch_sklearn</span><span class="p">()</span>
 
-<span class="c1"># You need to re-import scikit-learn algorithms after the patch</span>
-<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
 
-<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">0</span><span class="p">],</span>
-              <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;kmeans.labels_ = </span><span class="si">{</span><span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>In the example above, you can see that the use of the original Scikit-learn
-has not changed. This behavior is achieved through drop-in patching.</p>
-<p>To <a class="reference internal" href="what-is-patching.html#term-unpatching"><span class="xref std std-term">undo the patch</span></a>, run:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sklearnex</span><span class="o">.</span><span class="n">unpatch_sklearn</span><span class="p">()</span>
-<span class="c1"># Re-import scikit-learn algorithms after the unpatch:</span>
-<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
-</pre></div>
-</div>
-<p>You may specify which algorithms to patch:</p>
-<ul>
-<li><p>Patching only one algorithm:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
-<span class="c1"># The names match scikit-learn estimators</span>
-<span class="n">patch_sklearn</span><span class="p">(</span><span class="s2">&quot;SVC&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</li>
-<li><p>Patching several algorithms:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
-<span class="c1"># The names match scikit-learn estimators</span>
-<span class="n">patch_sklearn</span><span class="p">([</span><span class="s2">&quot;SVC&quot;</span><span class="p">,</span> <span class="s2">&quot;DBSCAN&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</li>
-</ul>
 </section>
-<section id="important-links">
-<h2>Important Links<a class="headerlink" href="#important-links" title="Permalink to this heading"></a></h2>
-<ul class="simple">
-<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex">Intel® Extension for Scikit-learn* repository</a></p></li>
-<li><p><a class="reference external" href="https://github.com/IntelPython/scikit-learn_bench">Machine Learning Benchmarks</a></p></li>
-<li><p><a class="reference external" href="https://github.com/oneapi-src/oneDAL">About Intel(R) oneAPI Data Analytics Library</a></p></li>
-<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/master/daal4py">About daal4py</a></p></li>
-</ul>
-<div class="toctree-wrapper compound">
-</div>
-<div class="toctree-wrapper compound">
-</div>
-<div class="toctree-wrapper compound">
-</div>
-<div class="toctree-wrapper compound">
+<section id="enable-intel-r-gpu-optimizations">
+<h3>Enable Intel(R) GPU optimizations<a class="headerlink" href="#enable-intel-r-gpu-optimizations" title="Link to this heading"></a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">dpctl</span>
+<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span><span class="p">,</span> <span class="n">config_context</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">DBSCAN</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
+            <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="p">[</span><span class="mf">25.</span><span class="p">,</span> <span class="mf">80.</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">target_offload</span><span class="o">=</span><span class="s2">&quot;gpu:0&quot;</span><span class="p">):</span>
+    <span class="n">clustering</span> <span class="o">=</span> <span class="n">DBSCAN</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
 </div>
 <div class="toctree-wrapper compound">
 </div>
@@ -248,6 +478,7 @@ <h2>Important Links<a class="headerlink" href="#important-links" title="Permalin
 <div class="toctree-wrapper compound">
 </div>
 </section>
+</section>
 </section>
 
 
@@ -260,7 +491,7 @@ <h2>Important Links<a class="headerlink" href="#important-links" title="Permalin
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0c42c40>
+<jinja2.runtime.BlockReference object at 0x7f04f0435e20>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -285,6 +516,28 @@ <h2>Important Links<a class="headerlink" href="#important-links" title="Permalin
       </div>
     </section>
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+  <div class="version-switcher__container dropdown">
+    <script>
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diff --git a/latest/input-types.html b/latest/input-types.html
new file mode 100644
index 0000000000..4dd6cec231
--- /dev/null
+++ b/latest/input-types.html
@@ -0,0 +1,507 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
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+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Supported input types &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
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+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
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+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+            Intel(R) Extension for Scikit-learn*
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+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
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+      current_version = "2025.1"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
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+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li class="breadcrumb-item active">Supported input types</li>
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+            <a href="_sources/input-types.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="supported-input-types">
+<span id="input-types"></span><h1>Supported input types<a class="headerlink" href="#supported-input-types" title="Link to this heading"></a></h1>
+<p>Just like Scikit-Learn estimators, estimators from Intel(R)’s Extension are able to accept
+and work with different classes of input data, including:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://numpy.org/">Numpy</a> arrays.</p></li>
+<li><p>Other array classes implementing the <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API</a> protocol
+(see <a class="reference internal" href="array_api.html#array-api"><span class="std std-ref">Array API support</span></a> for details).</p></li>
+<li><p><a class="reference external" href="https://docs.scipy.org/doc/scipy/tutorial/sparse.html">SciPy sparse arrays and sparse matrices</a> (depending on the estimator).</p></li>
+<li><p><a class="reference external" href="https://pandas.pydata.org">Pandas</a> <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and <code class="docutils literal notranslate"><span class="pre">Series</span></code> classes.</p></li>
+</ul>
+<p>In addition, Intel(R)’s Extension also supports:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/IntelPython/dpnp">dpnp</a> arrays.</p></li>
+<li><p><a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> arrays.</p></li>
+</ul>
+<p>Stock Scikit-Learn estimators, depending on the version, might offer support for additional
+input types beyond this list, such as <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and <code class="docutils literal notranslate"><span class="pre">Series</span></code> classes from other libraries
+like <a class="reference external" href="https://pola.rs">Polars</a>.</p>
+<p>Intel(R)’s Extension currently does not offer accelerated routines for input types not listed
+here - when receiving an unsupported class, estimators will fall back to stock Scikit-Learn to
+handle it, so make sure to convert them to a supported type when using Intel(R)’s Extension.</p>
+</section>
+
+
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Kaggle Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
 </li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -140,14 +416,18 @@
            <div itemprop="articleBody">
              
   <section id="kaggle-kernels">
-<span id="kaggle"></span><h1>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Permalink to this heading"></a></h1>
-<p>Check out Kaggle notebooks created by Intel® Extension for Scikit-learn* developers.</p>
-<p class="rubric">Acceleration</p>
+<span id="kaggle"></span><h1>Kaggle Kernels<a class="headerlink" href="#kaggle-kernels" title="Link to this heading"></a></h1>
+<p>See Kaggle notebooks created by Intel® Extension for Scikit-learn* developers.</p>
+<section id="acceleration">
+<h2>Acceleration<a class="headerlink" href="#acceleration" title="Link to this heading"></a></h2>
 <p><a class="reference external" href="https://www.kaggle.com/code/lordozvlad/introduction-to-scikit-learn-intelex/notebook">Introduction to scikit-learn-intelex</a>
 provides a summary of the speedup you can achieve with Intel® Extension for Scikit-learn*.</p>
-<p class="rubric">Machine Learning Workflows</p>
+</section>
+<section id="machine-learning-workflows">
+<h2>Machine Learning Workflows<a class="headerlink" href="#machine-learning-workflows" title="Link to this heading"></a></h2>
 <p>Browse this chapter to find Kaggle kernels that use scikit-learn-intelex for a specific type of a machine learning task.</p>
-<p>Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</p>
+<section id="kaggle-kernels-that-use-scikit-learn-and-intelex">
+<h3>Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-scikit-learn-and-intelex" title="Link to this heading"></a></h3>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
@@ -164,26 +444,30 @@
 </li>
 </ul>
 </div>
-<p>Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</p>
+</section>
+<section id="kaggle-kernels-that-use-automl-with-intelex">
+<h3>Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:<a class="headerlink" href="#kaggle-kernels-that-use-automl-with-intelex" title="Link to this heading"></a></h3>
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+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
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+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#binary-classification">Binary Classification</a></li>
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+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul class="current">
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+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">AutoML Workflows</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
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 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
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@@ -141,13 +417,13 @@
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   <section id="kaggle-kernels-that-use-automl-and-intelex">
-<h1>Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*<a class="headerlink" href="#kaggle-kernels-that-use-automl-and-intelex" title="Permalink to this heading"></a></h1>
+<h1>Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*<a class="headerlink" href="#kaggle-kernels-that-use-automl-and-intelex" title="Link to this heading"></a></h1>
 <p>The following Kaggle kernels show how to patch autoML frameworks with Intel® Extension for Scikit-learn*.</p>
 <p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
 <table class="docutils align-left">
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+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
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+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="regression.html#stacking-regressors">Stacking Regressors</a></li>
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+</li>
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+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
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+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Classification Tasks</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#binary-classification">Binary Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#multiclass-classification">MultiClass Classification</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="regression.html">Regression Tasks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
 </li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -147,17 +417,17 @@
            <div itemprop="articleBody">
              
   <section id="kaggle-kernels-for-classification-tasks">
-<h1>Kaggle Kernels for Classification Tasks<a class="headerlink" href="#kaggle-kernels-for-classification-tasks" title="Permalink to this heading"></a></h1>
+<h1>Kaggle Kernels for Classification Tasks<a class="headerlink" href="#kaggle-kernels-for-classification-tasks" title="Link to this heading"></a></h1>
 <p>The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various classification tasks.
 These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.</p>
 <p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
 <section id="binary-classification">
-<h2>Binary Classification<a class="headerlink" href="#binary-classification" title="Permalink to this heading"></a></h2>
+<h2>Binary Classification<a class="headerlink" href="#binary-classification" title="Link to this heading"></a></h2>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 38%" />
-<col style="width: 25%" />
-<col style="width: 38%" />
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Kernel</p></th>
@@ -230,12 +500,12 @@ <h2>Binary Classification<a class="headerlink" href="#binary-classification" tit
 </table>
 </section>
 <section id="multiclass-classification">
-<h2>MultiClass Classification<a class="headerlink" href="#multiclass-classification" title="Permalink to this heading"></a></h2>
+<h2>MultiClass Classification<a class="headerlink" href="#multiclass-classification" title="Link to this heading"></a></h2>
 <table class="docutils align-left">
 <colgroup>
-<col style="width: 38%" />
-<col style="width: 25%" />
-<col style="width: 38%" />
+<col style="width: 37.5%" />
+<col style="width: 25.0%" />
+<col style="width: 37.5%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>Kernel</p></th>
@@ -256,7 +526,7 @@ <h2>MultiClass Classification<a class="headerlink" href="#multiclass-classificat
 </ul>
 </td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex">Support Vector Classification (SVC) for MultiClass Classification</a></p>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/napetrov/svm-tps-may-2021-with-scikit-learn-intelex">Support Vector Classification (SVC) for MultiClass Classification (example 1)</a></p>
 <p><strong>Data:</strong> [TPS May 2021] Synthetic eCommerce data</p>
 </td>
 <td><p>Predict the category of an eCommerce product</p></td>
@@ -280,7 +550,7 @@ <h2>MultiClass Classification<a class="headerlink" href="#multiclass-classificat
 </ul>
 </td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup">Support Vector Classification (SVC) for MultiClass Classification</a></p>
+<tr class="row-odd"><td><p><a class="reference external" href="https://www.kaggle.com/code/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup">Support Vector Classification (SVC) for MultiClass Classification (example 2)</a></p>
 <p><strong>Data:</strong> [TPS Dec 2021] Synthetic Forest Cover Type data</p>
 </td>
 <td><p>Predict the forest cover type</p></td>
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 </table>
 </section>
 <section id="classification-tasks-in-computer-vision">
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 </table>
 </section>
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+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="classification.html">Classification Tasks</a><ul>
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+<li class="toctree-l5"><a class="reference internal" href="classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
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-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
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-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
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-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="classification.html">Classification Tasks</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Regression Tasks</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#using-a-single-regressor">Using a Single Regressor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#stacking-regressors">Stacking Regressors</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="automl.html">AutoML Workflows</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
 </ul>
 </li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
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+
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@@ -145,17 +417,17 @@
            <div itemprop="articleBody">
              
   <section id="kaggle-kernels-for-regression-tasks">
-<h1>Kaggle Kernels for Regression Tasks<a class="headerlink" href="#kaggle-kernels-for-regression-tasks" title="Permalink to this heading"></a></h1>
+<h1>Kaggle Kernels for Regression Tasks<a class="headerlink" href="#kaggle-kernels-for-regression-tasks" title="Link to this heading"></a></h1>
 <p>The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various regression tasks.
 These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.</p>
 <p>TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.</p>
 <section id="using-a-single-regressor">
-<h2>Using a Single Regressor<a class="headerlink" href="#using-a-single-regressor" title="Permalink to this heading"></a></h2>
+<h2>Using a Single Regressor<a class="headerlink" href="#using-a-single-regressor" title="Link to this heading"></a></h2>
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+
+
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             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
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+            Intel(R) Extension for Scikit-learn*
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
+</ul>
+
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">Intel(R) Extension for Scikit-learn*</a>
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+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Non-Scikit-Learn Algorithms</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/non-scikit-algorithms.rst.txt" rel="nofollow"> View page source</a>
+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="non-scikit-learn-algorithms">
+<h1>Non-Scikit-Learn Algorithms<a class="headerlink" href="#non-scikit-learn-algorithms" title="Link to this heading"></a></h1>
+<p>Algorithms not presented in the original scikit-learn are described here. All algorithms are
+available for both CPU and GPU (including distributed mode)</p>
+<section id="basicstatistics">
+<h2>BasicStatistics<a class="headerlink" href="#basicstatistics" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.basic_statistics.</span></span><span class="sig-name descname"><span class="pre">BasicStatistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/basic_statistics.html#BasicStatistics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics" title="Link to this definition"></a></dt>
+<dd><p>Estimator for basic statistics.
+Allows to compute basic statistics for provided data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>result_options</strong> (<em>string</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>, </em><em>default='all'</em>) – Used to set statistics to calculate. Possible values are <code class="docutils literal notranslate"><span class="pre">'min'</span></code>, <code class="docutils literal notranslate"><span class="pre">'max'</span></code>, <code class="docutils literal notranslate"><span class="pre">'sum'</span></code>, <code class="docutils literal notranslate"><span class="pre">'mean'</span></code>, <code class="docutils literal notranslate"><span class="pre">'variance'</span></code>,
+<code class="docutils literal notranslate"><span class="pre">'variation'</span></code>, <code class="docutils literal notranslate"><span class="pre">sum_squares'</span></code>, <code class="docutils literal notranslate"><span class="pre">sum_squares_centered'</span></code>, <code class="docutils literal notranslate"><span class="pre">'standard_deviation'</span></code>, <code class="docutils literal notranslate"><span class="pre">'second_order_raw_moment'</span></code>
+or a list containing any of these values. If set to <code class="docutils literal notranslate"><span class="pre">'all'</span></code> then all possible statistics will be
+calculated.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use in parallel for the computation.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means using all physical cores
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all logical cores.
+See <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-n_jobs" title="(in scikit-learn v1.6)"><span class="xref std std-term">Glossary</span></a> for more details.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.min_">
+<span class="sig-name descname"><span class="pre">min_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.min_" title="Link to this definition"></a></dt>
+<dd><p>Minimum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.max_">
+<span class="sig-name descname"><span class="pre">max_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.max_" title="Link to this definition"></a></dt>
+<dd><p>Maximum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.sum_">
+<span class="sig-name descname"><span class="pre">sum_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.sum_" title="Link to this definition"></a></dt>
+<dd><p>Sum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.mean_">
+<span class="sig-name descname"><span class="pre">mean_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.mean_" title="Link to this definition"></a></dt>
+<dd><p>Mean of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.variance_">
+<span class="sig-name descname"><span class="pre">variance_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.variance_" title="Link to this definition"></a></dt>
+<dd><p>Variance of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.variation_">
+<span class="sig-name descname"><span class="pre">variation_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.variation_" title="Link to this definition"></a></dt>
+<dd><p>Variation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.sum_squares_">
+<span class="sig-name descname"><span class="pre">sum_squares_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_" title="Link to this definition"></a></dt>
+<dd><p>Sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.standard_deviation_">
+<span class="sig-name descname"><span class="pre">standard_deviation_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.standard_deviation_" title="Link to this definition"></a></dt>
+<dd><p>Standard deviation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_">
+<span class="sig-name descname"><span class="pre">sum_squares_centered_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_" title="Link to this definition"></a></dt>
+<dd><p>Centered sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_">
+<span class="sig-name descname"><span class="pre">second_order_raw_moment_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_" title="Link to this definition"></a></dt>
+<dd><p>Second order moment of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Attribute exists only if corresponding result option has been provided.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Names of attributes without the trailing underscore are
+supported currently but deprecated in 2025.1 and will be removed in 2026.0</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Some results can exhibit small variations due to
+floating point error accumulation and multithreading.</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.basic_statistics</span> <span class="kn">import</span> <span class="n">BasicStatistics</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span> <span class="o">=</span> <span class="n">BasicStatistics</span><span class="p">(</span><span class="n">result_options</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sum&#39;</span><span class="p">,</span> <span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span><span class="o">.</span><span class="n">sum_</span>
+<span class="go">np.array([4., 6.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bs</span><span class="o">.</span><span class="n">min_</span>
+<span class="go">np.array([1., 2.])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.BasicStatistics.fit">
+<span class="sig-prename descclassname"><span class="pre">BasicStatistics.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/basic_statistics.html#BasicStatistics.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.BasicStatistics.fit" title="Link to this definition"></a></dt>
+<dd><p>Calculate statistics of X.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Data for compute, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>sample_weight</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Weights for compute weighted statistics, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="incrementalbasicstatistics">
+<h2>IncrementalBasicStatistics<a class="headerlink" href="#incrementalbasicstatistics" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.basic_statistics.</span></span><span class="sig-name descname"><span class="pre">IncrementalBasicStatistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/incremental_basic_statistics.html#IncrementalBasicStatistics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics" title="Link to this definition"></a></dt>
+<dd><p>Calculates basic statistics on the given data, allows for computation when the data are split into
+batches. The user can use <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method to provide a single batch of data or use the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method to provide
+the entire dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>result_options</strong> (<em>string</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>, </em><em>default='all'</em>) – List of statistics to compute</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of samples to use for each batch. Only used when calling
+<code class="docutils literal notranslate"><span class="pre">fit</span></code>. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>
+is inferred from the data and set to <code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">*</span> <span class="pre">n_features</span></code>.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use in parallel for the computation.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means using all physical cores
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all logical cores.
+See <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-n_jobs" title="(in scikit-learn v1.6)"><span class="xref std std-term">Glossary</span></a> for more details.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.min_">
+<span class="sig-name descname"><span class="pre">min_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.min_" title="Link to this definition"></a></dt>
+<dd><p>Minimum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.max_">
+<span class="sig-name descname"><span class="pre">max_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.max_" title="Link to this definition"></a></dt>
+<dd><p>Maximum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.sum_">
+<span class="sig-name descname"><span class="pre">sum_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_" title="Link to this definition"></a></dt>
+<dd><p>Sum of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.mean_">
+<span class="sig-name descname"><span class="pre">mean_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_" title="Link to this definition"></a></dt>
+<dd><p>Mean of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.variance_">
+<span class="sig-name descname"><span class="pre">variance_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_" title="Link to this definition"></a></dt>
+<dd><p>Variance of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.variation_">
+<span class="sig-name descname"><span class="pre">variation_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_" title="Link to this definition"></a></dt>
+<dd><p>Variation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_">
+<span class="sig-name descname"><span class="pre">sum_squares_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_" title="Link to this definition"></a></dt>
+<dd><p>Sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_">
+<span class="sig-name descname"><span class="pre">standard_deviation_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_" title="Link to this definition"></a></dt>
+<dd><p>Standard deviation of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_">
+<span class="sig-name descname"><span class="pre">sum_squares_centered_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_" title="Link to this definition"></a></dt>
+<dd><p>Centered sum of squares for each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_">
+<span class="sig-name descname"><span class="pre">second_order_raw_moment_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_" title="Link to this definition"></a></dt>
+<dd><p>Second order moment of each feature over all samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_">
+<span class="sig-name descname"><span class="pre">n_samples_seen_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_" title="Link to this definition"></a></dt>
+<dd><p>The number of samples processed by the estimator. Will be reset on
+new calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>, but increments across <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> calls.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_">
+<span class="sig-name descname"><span class="pre">batch_size_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_" title="Link to this definition"></a></dt>
+<dd><p>Inferred batch size from <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_">
+<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_" title="Link to this definition"></a></dt>
+<dd><p>Number of features seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> or  <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Attribute exists only if corresponding result option has been provided.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Names of attributes without the trailing underscore are
+supported currently but deprecated in 2025.1 and will be removed in 2026.0</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.basic_statistics</span> <span class="kn">import</span> <span class="n">IncrementalBasicStatistics</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span> <span class="o">=</span> <span class="n">IncrementalBasicStatistics</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">sum_</span>
+<span class="go">np.array([4., 6.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">min_</span>
+<span class="go">np.array([1., 2.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">sum_</span>
+<span class="go">np.array([4., 6.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">incbs</span><span class="o">.</span><span class="n">max_</span>
+<span class="go">np.array([3., 4.])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalBasicStatistics.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/incremental_basic_statistics.html#IncrementalBasicStatistics.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.fit" title="Link to this definition"></a></dt>
+<dd><p>Calculate statistics of X using minibatches of size batch_size.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Data for compute, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>sample_weight</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Weights for compute weighted statistics, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalBasicStatistics.</span></span><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/basic_statistics/incremental_basic_statistics.html#IncrementalBasicStatistics.partial_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit" title="Link to this definition"></a></dt>
+<dd><p>Incremental fit with X. All of X is processed as a single batch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Data for compute, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>sample_weight</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em><em>, </em><em>default=None</em>) – Weights for compute weighted statistics, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples.</p></li>
+<li><p><strong>check_input</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – Run <code class="docutils literal notranslate"><span class="pre">check_array</span></code> on X.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="incrementalempiricalcovariance">
+<h2>IncrementalEmpiricalCovariance<a class="headerlink" href="#incrementalempiricalcovariance" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.covariance.</span></span><span class="sig-name descname"><span class="pre">IncrementalEmpiricalCovariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/covariance/incremental_covariance.html#IncrementalEmpiricalCovariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance" title="Link to this definition"></a></dt>
+<dd><p>Maximum likelihood covariance estimator that allows for the estimation when the data are split into
+batches. The user can use the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method to provide a single batch of data or use the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method to provide
+the entire dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>store_precision</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=False</em>) – Specifies if the estimated precision is stored.</p></li>
+<li><p><strong>assume_centered</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=False</em>) – If True, data are not centered before computation.
+Useful when working with data whose mean is almost, but not exactly
+zero.
+If False (default), data are centered before computation.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of samples to use for each batch. Only used when calling
+<code class="docutils literal notranslate"><span class="pre">fit</span></code>. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>
+is inferred from the data and set to <code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">*</span> <span class="pre">n_features</span></code>, to provide a
+balance between approximation accuracy and memory consumption.</p></li>
+<li><p><strong>copy</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – If False, X will be overwritten. <code class="docutils literal notranslate"><span class="pre">copy=False</span></code> can be used to
+save memory but is unsafe for general use.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use in parallel for the computation.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means using all physical cores
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all logical cores.
+See <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-n_jobs" title="(in scikit-learn v1.6)"><span class="xref std std-term">Glossary</span></a> for more details.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.location_">
+<span class="sig-name descname"><span class="pre">location_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.location_" title="Link to this definition"></a></dt>
+<dd><p>Estimated location, i.e. the estimated mean.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_">
+<span class="sig-name descname"><span class="pre">covariance_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_" title="Link to this definition"></a></dt>
+<dd><p>Estimated covariance matrix</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of shape (n_features, n_features)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_">
+<span class="sig-name descname"><span class="pre">n_samples_seen_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_" title="Link to this definition"></a></dt>
+<dd><p>The number of samples processed by the estimator. Will be reset on
+new calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>, but increments across <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> calls.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_">
+<span class="sig-name descname"><span class="pre">batch_size_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_" title="Link to this definition"></a></dt>
+<dd><p>Inferred batch size from <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_">
+<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_" title="Link to this definition"></a></dt>
+<dd><p>Number of features seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.covariance</span> <span class="kn">import</span> <span class="n">IncrementalEmpiricalCovariance</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span> <span class="o">=</span> <span class="n">IncrementalEmpiricalCovariance</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">covariance_</span>
+<span class="go">np.array([[1., 1.],[1., 1.]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">location_</span>
+<span class="go">np.array([2., 3.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">covariance_</span>
+<span class="go">np.array([[1., 1.],[1., 1.]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inccov</span><span class="o">.</span><span class="n">location_</span>
+<span class="go">np.array([2., 3.])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalEmpiricalCovariance.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/covariance/incremental_covariance.html#IncrementalEmpiricalCovariance.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.fit" title="Link to this definition"></a></dt>
+<dd><p>Fit the model with X, using minibatches of size batch_size.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <cite>n_samples</cite> is the number of samples and
+<cite>n_features</cite> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalEmpiricalCovariance.</span></span><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/covariance/incremental_covariance.html#IncrementalEmpiricalCovariance.partial_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit" title="Link to this definition"></a></dt>
+<dd><p>Incremental fit with X. All of X is processed as a single batch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <cite>n_samples</cite> is the number of samples and
+<cite>n_features</cite> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+<li><p><strong>check_input</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – Run check_array on X.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="incrementallinearregression">
+<h2>IncrementalLinearRegression<a class="headerlink" href="#incrementallinearregression" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearnex.linear_model.</span></span><span class="sig-name descname"><span class="pre">IncrementalLinearRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression" title="Link to this definition"></a></dt>
+<dd><p>Trains a linear regression model, allows for computation if the data are split into
+batches. The user can use the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method to provide a single batch of data or use the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method to provide
+the entire dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fit_intercept</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>)</p></li>
+<li><p><strong>set</strong> (<em>Whether to calculate the intercept for this model. If</em>)</p></li>
+<li><p><strong>False</strong> (<em>to</em>)</p></li>
+<li><p><strong>calculations</strong> (<em>no intercept will be used in</em>)</p></li>
+<li><p><strong>centered</strong><strong>)</strong><strong>.</strong> (<em>(</em><em>i.e. data is expected to be</em>)</p></li>
+<li><p><strong>copy_X</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>default=True</em>) – If True, X will be copied; else, it may be overwritten.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of jobs to use for the computation.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>default=None</em>) – The number of samples to use for each batch. Only used when calling
+<code class="docutils literal notranslate"><span class="pre">fit</span></code>. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>
+is inferred from the data and set to <code class="docutils literal notranslate"><span class="pre">5</span> <span class="pre">*</span> <span class="pre">n_features</span></code>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.coef_">
+<span class="sig-name descname"><span class="pre">coef_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.coef_" title="Link to this definition"></a></dt>
+<dd><p>Estimated coefficients for the linear regression problem.
+If multiple targets are passed during the fit (y 2D), this
+is a 2D array of shape (n_targets, n_features), while if only
+one target is passed, this is a 1D array of length n_features.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array of shape (n_features, ) or (n_targets, n_features)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.intercept_">
+<span class="sig-name descname"><span class="pre">intercept_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.intercept_" title="Link to this definition"></a></dt>
+<dd><p>Independent term in the linear model. Set to 0.0 if
+<cite>fit_intercept = False</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a> or array of shape (n_targets,)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_">
+<span class="sig-name descname"><span class="pre">n_samples_seen_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_" title="Link to this definition"></a></dt>
+<dd><p>The number of samples processed by the estimator. Will be reset on
+new calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code>, but increments across <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> calls.
+It should be not less than <cite>n_features_in_</cite> if <cite>fit_intercept</cite>
+is False and not less than <cite>n_features_in_</cite> + 1 if <cite>fit_intercept</cite>
+is True to obtain regression coefficients.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.batch_size_">
+<span class="sig-name descname"><span class="pre">batch_size_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.batch_size_" title="Link to this definition"></a></dt>
+<dd><p>Inferred batch size from <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.n_features_in_">
+<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_" title="Link to this definition"></a></dt>
+<dd><p>Number of features seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearnex.linear_model</span> <span class="kn">import</span> <span class="n">IncrementalLinearRegression</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span> <span class="o">=</span> <span class="n">IncrementalLinearRegression</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">10</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">,</span> <span class="mf">8.5</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span> <span class="n">y</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span> <span class="n">y</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">coef_</span>
+<span class="go">np.array([0.5., 0.5.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">intercept_</span>
+<span class="go">np.array(0.)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">coef_</span>
+<span class="go">np.array([0.5., 0.5.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inclr</span><span class="o">.</span><span class="n">intercept_</span>
+<span class="go">np.array(0.)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalLinearRegression.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.fit" title="Link to this definition"></a></dt>
+<dd><p>Fit the model with X and y, using minibatches of size <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features. It is necessary for
+<code class="docutils literal notranslate"><span class="pre">n_samples</span></code> to be not less than <code class="docutils literal notranslate"><span class="pre">n_features</span></code> if <code class="docutils literal notranslate"><span class="pre">fit_intercept</span></code>
+is False and not less than <code class="docutils literal notranslate"><span class="pre">n_features</span> <span class="pre">+</span> <span class="pre">1</span></code> if <code class="docutils literal notranslate"><span class="pre">fit_intercept</span></code>
+is True</p></li>
+<li><p><strong>y</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>) or </em><em>(</em><em>n_samples</em><em>, </em><em>n_targets</em><em>)</em>) – Target values, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_targets</span></code> is the number of targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.partial_fit">
+<span class="sig-prename descclassname"><span class="pre">IncrementalLinearRegression.</span></span><span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression.partial_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.partial_fit" title="Link to this definition"></a></dt>
+<dd><p>Incremental fit linear model with X and y. All of X and y is
+processed as a single batch.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Training data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<cite>n_features</cite> is the number of features.</p></li>
+<li><p><strong>y</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>) or </em><em>(</em><em>n_samples</em><em>, </em><em>n_targets</em><em>)</em>) – Target values, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
+<code class="docutils literal notranslate"><span class="pre">n_targets</span></code> is the number of targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – Returns the instance itself.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)">object</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearnex.linear_model.IncrementalLinearRegression.predict">
+<span class="sig-prename descclassname"><span class="pre">IncrementalLinearRegression.</span></span><span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sklearnex/linear_model/incremental_linear.html#IncrementalLinearRegression.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearnex.linear_model.IncrementalLinearRegression.predict" title="Link to this definition"></a></dt>
+<dd><p>Predict using the linear model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em><em> or </em><em>sparse matrix</em><em>, </em><em>shape</em><em> (</em><em>n_samples</em><em>, </em><em>n_features</em><em>)</em>) – Samples.</p></li>
+<li><p><strong>y</strong> (<em>Ignored</em>) – Not used, present for API consistency by convention.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>C</strong> – Returns predicted values.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array, shape (n_samples, n_targets)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="distributed-mode.html" class="btn btn-neutral float-left" title="Distributed Mode" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+++ b/latest/oneapi-gpu.html
@@ -1,23 +1,30 @@
+
+
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@@ -51,10 +59,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
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 <ul>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">oneAPI and GPU support</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#device-offloading">Device offloading</a></li>
@@ -79,42 +268,129 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -140,14 +416,14 @@
            <div itemprop="articleBody">
              
   <section id="oneapi-and-gpu-support-in-intelex">
-<span id="oneapi-gpu"></span><h1>oneAPI and GPU support in Intel® Extension for Scikit-learn*<a class="headerlink" href="#oneapi-and-gpu-support-in-intelex" title="Permalink to this heading"></a></h1>
+<span id="oneapi-gpu"></span><h1>oneAPI and GPU support in Intel® Extension for Scikit-learn*<a class="headerlink" href="#oneapi-and-gpu-support-in-intelex" title="Link to this heading"></a></h1>
 <p>Intel® Extension for Scikit-learn* supports oneAPI concepts, which
 means that algorithms can be executed on different devices: CPUs and GPUs.
 This is done via integration with
 <a class="reference external" href="https://intelpython.github.io/dpctl/latest/index.html">dpctl</a> package that
 implements core oneAPI concepts like queues and devices.</p>
 <section id="prerequisites">
-<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this heading"></a></h2>
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading"></a></h2>
 <p>For execution on GPU, DPC++ compiler runtime and driver are required. Refer to <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html">DPC++ system
 requirements</a> for details.</p>
 <p>DPC++ compiler runtime can be installed either from PyPI or Anaconda:</p>
@@ -157,15 +433,15 @@ <h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to
 </pre></div>
 </div>
 </li>
-<li><p>Install from Anaconda:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">dpcpp_cpp_rt</span> <span class="o">-</span><span class="n">c</span> <span class="n">intel</span>
+<li><p>Install using Conda via the Intel repository:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">dpcpp_cpp_rt</span> <span class="o">-</span><span class="n">c</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">software</span><span class="o">.</span><span class="n">repos</span><span class="o">.</span><span class="n">intel</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">python</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span>
 </pre></div>
 </div>
 </li>
 </ul>
 </section>
 <section id="device-offloading">
-<h2>Device offloading<a class="headerlink" href="#device-offloading" title="Permalink to this heading"></a></h2>
+<h2>Device offloading<a class="headerlink" href="#device-offloading" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn* offers two options for running an algorithm on a
 specific device with the help of dpctl:</p>
 <ul>
@@ -215,7 +491,7 @@ <h2>Device offloading<a class="headerlink" href="#device-offloading" title="Perm
 described above for device offloading instead of using <code class="code docutils literal notranslate"><span class="pre">sycl_context</span></code>.</p>
 </section>
 <section id="example">
-<h2>Example<a class="headerlink" href="#example" title="Permalink to this heading"></a></h2>
+<h2>Example<a class="headerlink" href="#example" title="Link to this heading"></a></h2>
 <p>An example on how to patch your code with Intel CPU/GPU optimizations:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span><span class="p">,</span> <span class="n">config_context</span>
 <span class="n">patch_sklearn</span><span class="p">()</span>
@@ -248,7 +524,7 @@ <h2>Example<a class="headerlink" href="#example" title="Permalink to this headin
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0b73d30>
+<jinja2.runtime.BlockReference object at 0x7f04f038ba40>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -273,6 +549,28 @@ <h2>Example<a class="headerlink" href="#example" title="Permalink to this headin
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/latest/preview.html b/latest/preview.html
new file mode 100644
index 0000000000..8d733b72f0
--- /dev/null
+++ b/latest/preview.html
@@ -0,0 +1,547 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Preview Functionality &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
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+            Intel(R) Extension for Scikit-learn*
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+  <div class="version-switcher__container dropdown">
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+      current_version = "2025.1"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
+  
+  
+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
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+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+</ul>
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+      <li class="breadcrumb-item active">Preview Functionality</li>
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+            <a href="_sources/preview.rst.txt" rel="nofollow"> View page source</a>
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+  </ul>
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+           <div itemprop="articleBody">
+             
+  <section id="preview-functionality">
+<span id="preview"></span><h1>Preview Functionality<a class="headerlink" href="#preview-functionality" title="Link to this heading"></a></h1>
+<p>Some of implemented functionality in Intel® Extension for Scikit-learn* doesn’t meet one or few of next requirements
+for being enabled by default for all users:</p>
+<ul class="simple">
+<li><p>The functionality API is not stable and can be changed in future</p></li>
+<li><p>The functionality doesn’t have full compatibility with stock Scikit-learn</p></li>
+<li><p>The functionality misses performance targets compared to stock Scikit-learn or previously available version of functionality</p></li>
+<li><p>The functionality is not fully tested</p></li>
+</ul>
+<p>This type of functionality is available under <strong>preview mode</strong> of Intel® Extension for Scikit-learn* and located in
+the corresponding module (<cite>sklearnex.preview</cite>).</p>
+<p>Preview functionality <em>may</em> or <em>may not</em> participate in patching of Scikit-learn.
+For example, a preview estimator may be a replacement for a stock one or a completely new one.</p>
+<p>To enable preview functionality, you need to set the <cite>SKLEARNEX_PREVIEW</cite> environment variable
+to non-empty value before patching of Scikit-learn.
+For example, you can set the environment variable in the following way:</p>
+<ul>
+<li><p>On Linux* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">SKLEARNEX_PREVIEW</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+</li>
+<li><p>On Windows* OS</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span> <span class="n">SKLEARNEX_PREVIEW</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+</li>
+</ul>
+<p>Then, you can import Scikit-learn estimator patched with a preview one from <cite>sklearnex.preview</cite> module:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">()</span>
+<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">IncrementalPCA</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">IncrementalPCA</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span>
+<span class="c1"># output:</span>
+<span class="c1"># sklearnex.preview.decomposition.incremental_pca</span>
+</pre></div>
+</div>
+<p>Current list of preview estimators:</p>
+<table class="docutils align-left">
+<colgroup>
+<col style="width: 50.0%" />
+<col style="width: 33.3%" />
+<col style="width: 16.7%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Estimator name</p></th>
+<th class="head"><p>Module</p></th>
+<th class="head"><p>Is patching supported</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>EmpiricalCovariance</p></td>
+<td><p>sklearnex.preview.covariance</p></td>
+<td><p>Yes</p></td>
+</tr>
+<tr class="row-odd"><td><p>IncrementalPCA</p></td>
+<td><p>sklearnex.preview.decomposition</p></td>
+<td><p>Yes</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+           </div>
+          </div>
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -51,10 +61,6 @@
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+  <div class="version-switcher__container dropdown">
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 <ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Quick Start</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -135,90 +418,391 @@
            <div itemprop="articleBody">
              
   <section id="quick-start">
-<span id="id1"></span><h1>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this heading"></a></h1>
-<p>You may enable patching in different ways:</p>
-<div class="admonition important">
-<p class="admonition-title">Important</p>
-<p>These patching methods are interchangeable.
-They support different enabling scenarios while producing the same result.</p>
-</div>
-<ul>
-<li><p>Without editing the code of a scikit-learn application by using the following command line flag:</p>
+<h1>Quick Start<a class="headerlink" href="#quick-start" title="Link to this heading"></a></h1>
+<p>Get ready to elevate your scikit-learn code with Intel® Extension for Scikit-learn* and experience the benefits of accelerated performance in just a few simple steps.</p>
+<section id="compatibility-with-scikit-learn">
+<h2>Compatibility with Scikit-learn*<a class="headerlink" href="#compatibility-with-scikit-learn" title="Link to this heading"></a></h2>
+<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn.</p>
+</section>
+<section id="integrate-intelex">
+<h2>Integrate Intel® Extension for Scikit-learn*<a class="headerlink" href="#integrate-intelex" title="Link to this heading"></a></h2>
+<section id="patching">
+<h3>Patching<a class="headerlink" href="#patching" title="Link to this heading"></a></h3>
+<p>Once you install Intel*(R) Extension for Scikit-learn*, you replace algorithms that exist in the scikit-learn package with their optimized versions from the extension.
+This action is called <code class="docutils literal notranslate"><span class="pre">patching</span></code>. This is not a permanent change so you can always undo the patching if necessary.</p>
+<p>To patch Intel® Extension for Scikit-learn, use one of these methods:</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Method</p></th>
+<th class="head"><p>Action</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Use a flag in the command line</p></td>
+<td><p>Run this command:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span> <span class="n">my_application</span><span class="o">.</span><span class="n">py</span>
 </pre></div>
 </div>
-</li>
-<li><p>Directly from the script:</p>
+</td>
+</tr>
+<tr class="row-odd"><td><p>Modify your script</p></td>
+<td><p>Add the following lines:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
 <span class="n">patch_sklearn</span><span class="p">()</span>
 </pre></div>
 </div>
-</li>
-</ul>
-<div class="admonition important">
-<p class="admonition-title">Important</p>
-<p>You have to import scikit-learn <strong>after</strong> these lines. Otherwise, the patching will not affect the original scikit-learn estimators.</p>
-</div>
-<ul>
-<li><p>Through importing the desired estimator from the sklearnex module in your script:</p>
+</td>
+</tr>
+<tr class="row-even"><td><p>Import an estimator from the <code class="docutils literal notranslate"><span class="pre">sklearnex</span></code> module</p></td>
+<td><p>Run this command:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
 </pre></div>
 </div>
-</li>
-<li><p>Through <a class="reference internal" href="what-is-patching.html#global-patching"><span class="std std-ref">global patching</span></a> to enable patching for your scikit-learn installation for all further runs:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
-</pre></div>
-</div>
-</li>
-</ul>
-<p class="rubric">Example</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
-<span class="n">patch_sklearn</span><span class="p">()</span>
+</td>
+</tr>
+</tbody>
+</table>
+<p>These patching methods are interchangeable.
+They support different enabling scenarios while producing the same result.</p>
+<p><strong>Example</strong></p>
+<p>This example shows how to patch Intel(R) extension for Scikit-Learn by modifing your script. To make sure that patching is registered by the scikit-learn estimators, always import scikit-learn after these lines.</p>
+<div class="literal-block-wrapper docutils container" id="id2">
+<div class="code-block-caption"><span class="caption-text">Example: Drop-In Patching</span><a class="headerlink" href="#id2" title="Link to this code"></a></div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>  <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+  <span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+  <span class="n">patch_sklearn</span><span class="p">()</span>
 
-<span class="c1"># You need to re-import scikit-learn algorithms after the patch</span>
-<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
+  <span class="c1"># You need to re-import scikit-learn algorithms after the patch</span>
+  <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
 
-<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">0</span><span class="p">],</span>
+  <span class="c1"># The use of the original Scikit-learn is not changed</span>
+  <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span>  <span class="mi">0</span><span class="p">],</span>
               <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;kmeans.labels_ = </span><span class="si">{</span><span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+  <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+  <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;kmeans.labels_ = </span><span class="si">{</span><span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="global-patching">
+<h3>Global Patching<a class="headerlink" href="#global-patching" title="Link to this heading"></a></h3>
+<p>You can also use global patching to patch all your scikit-learn applications without any additional actions.</p>
+<p>Before you begin, make sure that you have read and write permissions for Scikit-learn files.</p>
+<p>With global patching, you can:</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Task</p></th>
+<th class="head"><p>Action</p></th>
+<th class="head"><p>Note</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Patch all supported algorithms</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span>
+</pre></div>
+</div>
+</td>
+<td><p>If you run the global patching command several times with different parameters, then only the last configuration is applied.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Patch selected algorithms</p></td>
+<td><p>Use <code class="docutils literal notranslate"><span class="pre">--algorithm</span></code> or <code class="docutils literal notranslate"><span class="pre">-a</span></code> keys with a list of algorithms to patch. For example, to patch only <code class="docutils literal notranslate"><span class="pre">SVC</span></code> and <code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> estimators, run</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>Enable global patching via code</p></td>
+<td><p>Use the <code class="docutils literal notranslate"><span class="pre">patch_sklearn</span></code> function with the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
+<span class="n">patch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">sklearn</span>
+</pre></div>
+</div>
+</td>
+<td><p>After that, Scikit-learn patches is enabled in the current application and in all others that use the same environment.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Disable patching notifications</p></td>
+<td><p>Use <code class="docutils literal notranslate"><span class="pre">--no-verbose</span></code> or <code class="docutils literal notranslate"><span class="pre">-nv</span></code> keys:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">patch_sklearn</span> <span class="o">-</span><span class="n">a</span> <span class="n">svc</span> <span class="n">random_forest_classifier</span> <span class="o">-</span><span class="n">nv</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>Disable global patching</p></td>
+<td><p>Run this command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">sklearnex</span><span class="o">.</span><span class="n">glob</span> <span class="n">unpatch_sklearn</span>
 </pre></div>
 </div>
-<p>In the example above, you can see that the use of the original Scikit-learn
-has not changed. This behavior is achieved through drop-in patching.</p>
-<p>To <a class="reference internal" href="what-is-patching.html#term-unpatching"><span class="xref std std-term">undo the patch</span></a>, run:</p>
+</td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p>Disable global patching via code</p></td>
+<td><p>Use the <code class="docutils literal notranslate"><span class="pre">global_patch</span></code> argument in the <code class="docutils literal notranslate"><span class="pre">unpatch_sklearn</span></code> function</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">unpatch_sklearn</span>
+<span class="n">unpatch_sklearn</span><span class="p">(</span><span class="n">global_patch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>If you clone an environment with enabled global patching, it will already be applied in the new environment.</p>
+</div>
+</section>
+<section id="unpatching">
+<h3>Unpatching<a class="headerlink" href="#unpatching" title="Link to this heading"></a></h3>
+<p>To undo the patch (also called <cite>unpatching</cite>) is to return scikit-learn to original implementation and
+replace patched algorithms with the stock scikit-learn algorithms.</p>
+<p>To unpatch successfully, you must reimport the scikit-learn package:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sklearnex</span><span class="o">.</span><span class="n">unpatch_sklearn</span><span class="p">()</span>
-<span class="c1"># Re-import scikit-learn algorithms after the unpatch:</span>
+<span class="c1"># Re-import scikit-learn algorithms after the unpatch</span>
 <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
 </pre></div>
 </div>
-<p>You may specify which algorithms to patch:</p>
-<ul>
-<li><p>Patching only one algorithm:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
-<span class="c1"># The names match scikit-learn estimators</span>
-<span class="n">patch_sklearn</span><span class="p">(</span><span class="s2">&quot;SVC&quot;</span><span class="p">)</span>
+</section>
+</section>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading"></a></h2>
+<nav class="contents local" id="contents">
+<ul class="simple">
+<li><p><a class="reference internal" href="#install-from-pypi" id="id6">Install from PyPI</a></p></li>
+<li><p><a class="reference internal" href="#install-from-anaconda-cloud" id="id7">Install from Anaconda* Cloud</a></p></li>
+<li><p><a class="reference internal" href="#build-from-sources" id="id8">Build from Sources</a></p></li>
+<li><p><a class="reference internal" href="#install-intel-r-ai-tools" id="id9">Install Intel*(R) AI Tools</a></p></li>
+</ul>
+</nav>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>To prevent version conflicts, we recommend creating and activating a new environment for Intel® Extension for Scikit-learn*.</p>
+</div>
+<section id="install-from-pypi">
+<h3><a class="toc-backref" href="#id6" role="doc-backlink">Install from PyPI</a><a class="headerlink" href="#install-from-pypi" title="Link to this heading"></a></h3>
+<p>Recommended by default.</p>
+<p>To install Intel® Extension for Scikit-learn*, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
 </pre></div>
 </div>
-</li>
-<li><p>Patching several algorithms:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearnex</span> <span class="kn">import</span> <span class="n">patch_sklearn</span>
-<span class="c1"># The names match scikit-learn estimators</span>
-<span class="n">patch_sklearn</span><span class="p">([</span><span class="s2">&quot;SVC&quot;</span><span class="p">,</span> <span class="s2">&quot;DBSCAN&quot;</span><span class="p">])</span>
+<p><strong>Supported Configurations</strong></p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="install-from-anaconda-cloud">
+<h3><a class="toc-backref" href="#id7" role="doc-backlink">Install from Anaconda* Cloud</a><a class="headerlink" href="#install-from-anaconda-cloud" title="Link to this heading"></a></h3>
+<p>To prevent version conflicts, we recommend installing <cite>scikit-learn-intelex</cite> into a new conda environment.</p>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-0-0-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-0-0-0" name="0-0" role="tab" tabindex="0">Conda-Forge channel</button><button aria-controls="panel-0-0-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-1" name="0-1" role="tab" tabindex="-1">Intel channel</button><button aria-controls="panel-0-0-2" aria-selected="false" class="sphinx-tabs-tab" id="tab-0-0-2" name="0-2" role="tab" tabindex="-1">Main channel</button></div><div aria-labelledby="tab-0-0-0" class="sphinx-tabs-panel" id="panel-0-0-0" name="0-0" role="tabpanel" tabindex="0"><p>Recommended by default.</p>
+<p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span>
 </pre></div>
 </div>
-</li>
+<table class="docutils align-left" id="id3">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id3" title="Link to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div><div aria-labelledby="tab-0-0-1" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-1" name="0-1" role="tabpanel" tabindex="0"><p>Recommended for the Intel® Distribution for Python users.</p>
+<p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span> <span class="o">-</span><span class="n">c</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">software</span><span class="o">.</span><span class="n">repos</span><span class="o">.</span><span class="n">intel</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">python</span><span class="o">/</span><span class="n">conda</span><span class="o">/</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id4">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id4" title="Link to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+<td><p>[CPU, GPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div><div aria-labelledby="tab-0-0-2" class="sphinx-tabs-panel" hidden="true" id="panel-0-0-2" name="0-2" role="tabpanel" tabindex="0"><p>To install, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">-</span><span class="n">intelex</span>
+</pre></div>
+</div>
+<table class="docutils align-left" id="id5">
+<caption><span class="caption-text"><strong>Supported Configurations</strong></span><a class="headerlink" href="#id5" title="Link to this table"></a></caption>
+<thead>
+<tr class="row-odd"><th class="head"><p>OS / Python version</p></th>
+<th class="head"><p>Python 3.9</p></th>
+<th class="head"><p>Python 3.10</p></th>
+<th class="head"><p>Python 3.11</p></th>
+<th class="head"><p>Python 3.12</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Linux* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+<tr class="row-odd"><td><p>Windows* OS</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+<td><p>[CPU]</p></td>
+</tr>
+</tbody>
+</table>
+</div></div>
+</section>
+<section id="build-from-sources">
+<h3><a class="toc-backref" href="#id8" role="doc-backlink">Build from Sources</a><a class="headerlink" href="#build-from-sources" title="Link to this heading"></a></h3>
+<p>See <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md">Installation instructions</a> to build Intel® Extension for Scikit-learn* from the sources.</p>
+</section>
+<section id="install-intel-r-ai-tools">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Install Intel*(R) AI Tools</a><a class="headerlink" href="#install-intel-r-ai-tools" title="Link to this heading"></a></h3>
+<p>Download the Intel AI Tools <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html">here</a>. The extension is already included.</p>
+</section>
+</section>
+<section id="release-notes">
+<h2>Release Notes<a class="headerlink" href="#release-notes" title="Link to this heading"></a></h2>
+<p>See the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/releases">Release Notes</a> for each version of Intel® Extension for Scikit-learn*.</p>
+</section>
+<section id="system-requirements">
+<h2>System Requirements<a class="headerlink" href="#system-requirements" title="Link to this heading"></a></h2>
+<section id="hardware-requirements">
+<h3>Hardware Requirements<a class="headerlink" href="#hardware-requirements" title="Link to this heading"></a></h3>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-1-1-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-1-1-0" name="1-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-1-1-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-1-1-1" name="1-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-1-1-0" class="sphinx-tabs-panel" id="panel-1-1-0" name="1-0" role="tabpanel" tabindex="0"><p>All processors with <code class="docutils literal notranslate"><span class="pre">x86</span></code> architecture with at least one of the following instruction sets:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>SSE2</p></li>
+<li><p>SSE4.2</p></li>
+<li><p>AVX2</p></li>
+<li><p>AVX512</p></li>
 </ul>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
+</div></blockquote>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>ARM* architecture is not supported.</p>
+</div>
+</div><div aria-labelledby="tab-1-1-1" class="sphinx-tabs-panel" hidden="true" id="panel-1-1-1" name="1-1" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>All Intel® integrated and discrete GPUs</p></li>
+<li><p>Intel® GPU drivers</p></li>
+</ul>
+</div></div>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>Intel(R) processors provide better performance than other CPUs. Read more about hardware comparison in our <a class="reference internal" href="blogs.html#blogs"><span class="std std-ref">blogs</span></a>.</p>
+</div>
+</section>
+<section id="software-requirements">
+<h3>Software Requirements<a class="headerlink" href="#software-requirements" title="Link to this heading"></a></h3>
+<div class="sphinx-tabs docutils container">
+<div aria-label="Tabbed content" class="closeable" role="tablist"><button aria-controls="panel-2-2-0" aria-selected="true" class="sphinx-tabs-tab" id="tab-2-2-0" name="2-0" role="tab" tabindex="0">CPU</button><button aria-controls="panel-2-2-1" aria-selected="false" class="sphinx-tabs-tab" id="tab-2-2-1" name="2-1" role="tab" tabindex="-1">GPU</button></div><div aria-labelledby="tab-2-2-0" class="sphinx-tabs-panel" id="panel-2-2-0" name="2-0" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
+<li><p>Windows* OS 10 or newer</p></li>
+<li><p>Windows* Server 2019 or newer</p></li>
+</ul>
+</div><div aria-labelledby="tab-2-2-1" class="sphinx-tabs-panel" hidden="true" id="panel-2-2-1" name="2-1" role="tabpanel" tabindex="0"><ul class="simple">
+<li><p>Linux* OS: Ubuntu* 18.04 or newer</p></li>
+<li><p>Windows* OS 10 or newer</p></li>
+<li><p>Windows* Server 2019 or newer</p></li>
+</ul>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<p>If you use accelerators, refer to <a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html">oneAPI DPC++/C++ Compiler System Requirements</a>.</p>
+</div>
+</div></div>
+<p>Intel(R) Extension for Scikit-learn is compatible with the last four versions of scikit-learn:</p>
 <ul class="simple">
-<li><p><a class="reference internal" href="algorithms.html#sklearn-algorithms"><span class="std std-ref">Supported algorithms and parameters</span></a></p></li>
-<li><p><a class="reference internal" href="samples.html#samples"><span class="std std-ref">Samples</span></a></p></li>
-<li><p><a class="reference internal" href="global-patching.html#global-patching"><span class="std std-ref">Global Patching</span></a></p></li>
-<li><p><a class="reference external" href="https://www.intel.com/content/www/us/en/developer/articles/guide/intel-extension-for-scikit-learn-getting-started.html">Getting Started Guide</a></p></li>
+<li><p>1.0.X</p></li>
+<li><p>1.1.X</p></li>
+<li><p>1.2.X</p></li>
+<li><p>1.3.X</p></li>
 </ul>
+</section>
+<section id="memory-requirements">
+<h3>Memory Requirements<a class="headerlink" href="#memory-requirements" title="Link to this heading"></a></h3>
+<p>By default, algorithms in Intel® Extension for Scikit-learn* run in the multi-thread mode. This mode uses all available threads.
+Optimized scikit-learn algorithms can consume more RAM than their corresponding unoptimized versions.</p>
+<table class="docutils align-left">
+<thead>
+<tr class="row-odd"><th class="head"><p>Algorithm</p></th>
+<th class="head"><p>Single-thread mode</p></th>
+<th class="head"><p>Multi-thread mode</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>SVM</p></td>
+<td><p>Both Scikit-learn and Intel® Extension for Scikit-learn* consume approximately the same amount of RAM.</p></td>
+<td><p>In Intel® Extension for Scikit-learn*, an algorithm with <code class="docutils literal notranslate"><span class="pre">N</span></code> threads consumes <code class="docutils literal notranslate"><span class="pre">N</span></code> times more RAM.</p></td>
+</tr>
+</tbody>
+</table>
+<p>In all Intel® Extension for Scikit-learn* algorithms with GPU support, computations run on device memory.
+The device memory must be large enough to store a copy of the entire dataset.
+You may also require additional device memory for internal arrays that are used in computation.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="samples.html#samples"><span class="std std-ref">Samples</span></a></p>
 </div>
+</section>
+</section>
 </section>
 
 
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+        <a href="samples.html" class="btn btn-neutral float-right" title="Samples" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
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     </section>
   </div>
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+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
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+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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diff --git a/latest/releases.html b/latest/releases.html
deleted file mode 100644
index a276283f07..0000000000
--- a/latest/releases.html
+++ /dev/null
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-              <div class="version">
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-                <p>Click link above to switch version</p>
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-        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
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-<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
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-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
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-  <section id="release-notes">
-<span id="releases"></span><h1>Release Notes<a class="headerlink" href="#release-notes" title="Permalink to this heading"></a></h1>
-<p>The updates of Intel® Extension for Scikit-learn* for each release can be found in <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/releases">Releases</a>.</p>
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diff --git a/latest/samples.html b/latest/samples.html
index 558fded632..3bab174d0e 100644
--- a/latest/samples.html
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@@ -1,30 +1,40 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-    <link rel="prev" title="Memory Requirements" href="memory-requirements.html" />
+    <link rel="prev" title="Quick Start" href="quick-start.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
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             Intel(R) Extension for Scikit-learn*
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@@ -63,68 +69,330 @@
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 </div>
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+  
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Samples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
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+</li>
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@@ -150,9 +418,9 @@
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   <section id="samples">
-<span id="id1"></span><h1>Samples<a class="headerlink" href="#samples" title="Permalink to this heading"></a></h1>
+<span id="id1"></span><h1>Samples<a class="headerlink" href="#samples" title="Link to this heading"></a></h1>
 <p>The following samples are also provided as Jupyter notebooks in Intel® Extension for Scikit-learn* repository.
-If you want to run them locally, refer to <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks">these instructions</a>.</p>
+If you want to run them locally, refer to <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks#snake-intelr-extension-for-scikit-learn-notebooks">these instructions</a>.</p>
 <p class="rubric">Classification Tasks</p>
 <div class="toctree-wrapper compound">
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@@ -179,6 +447,12 @@
 <li class="toctree-l1"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
 </ul>
 </div>
+<p class="rubric">Dimensionality Reduction Tasks</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a></li>
+</ul>
+</div>
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@@ -1,26 +1,33 @@
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+
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+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">ElasticNet for Airlines DepDelay dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -103,37 +146,254 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -160,7 +420,7 @@
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   <section id="Intel®-Extension-for-Scikit-learn-ElasticNet-for-Airlines-DepDelay-dataset">
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+<h1>Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-ElasticNet-for-Airlines-DepDelay-dataset" title="Link to this heading"></a></h1>
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 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
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 <section id="Preprocessing">
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+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
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 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
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 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
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 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
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+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
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 </div>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class ElasticNet</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
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diff --git a/latest/samples/ElasticNet.ipynb b/latest/samples/ElasticNet.ipynb
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+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
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+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -136,7 +417,7 @@
            <div itemprop="articleBody">
              
   <section id="Utilizing-daal4py-in-Data-Science-Workflows">
-<h1>Utilizing daal4py in Data Science Workflows<a class="headerlink" href="#Utilizing-daal4py-in-Data-Science-Workflows" title="Permalink to this heading"></a></h1>
+<h1>Utilizing daal4py in Data Science Workflows<a class="headerlink" href="#Utilizing-daal4py-in-Data-Science-Workflows" title="Link to this heading"></a></h1>
 <p>The notebook below has been made to demonstrate daal4py in a data science context. It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user’s Power output in the absence of a power meter.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
@@ -277,7 +558,7 @@ <h1>Utilizing daal4py in Data Science Workflows<a class="headerlink" href="#Util
 <div class="line">- Heart Rate is a measure of the body’s workout strain, and would have a high possibly of influence. - Distance may have a loose correlation as it is highly route dependent, but might be possible. - Speed has possible correlations as it ties directly into power.</div>
 </div>
 <section id="Explore-and-visualize-some-of-the-data">
-<h2>Explore and visualize some of the data<a class="headerlink" href="#Explore-and-visualize-some-of-the-data" title="Permalink to this heading"></a></h2>
+<h2>Explore and visualize some of the data<a class="headerlink" href="#Explore-and-visualize-some-of-the-data" title="Link to this heading"></a></h2>
 <p>In general, we are trying to predict on the ‘power’ in Watts to see if we can generate a model that can predict one’s power output without the usage of a cycling power meter. Below are some basic scatterplots as we explore the data. Scatterplots are great for looking for patterns and correlation in the data itself. Below, we can see that cadence and speed are positively correlated.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -342,7 +623,7 @@ <h2>Explore and visualize some of the data<a class="headerlink" href="#Explore-a
 </div>
 </section>
 <section id="Using-daal4py-for-Machine-Learning-tasks">
-<h2>Using daal4py for Machine Learning tasks<a class="headerlink" href="#Using-daal4py-for-Machine-Learning-tasks" title="Permalink to this heading"></a></h2>
+<h2>Using daal4py for Machine Learning tasks<a class="headerlink" href="#Using-daal4py-for-Machine-Learning-tasks" title="Link to this heading"></a></h2>
 <p>In the sections below, we will be using daal4py directly. After importing the model, we will arrange it in a separate independent and dependent dataframes, then use the daal4py’s training and prediction classes to generate a workable model.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -405,7 +686,7 @@ <h2>Using daal4py for Machine Learning tasks<a class="headerlink" href="#Using-d
 </div>
 </section>
 <section id="Training-the-model">
-<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Permalink to this heading"></a></h2>
+<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Link to this heading"></a></h2>
 <p>Create the Linear Regression Model, and train the model with the data. We utilize daal4py’s linear_regression_training class to create the model, then call .compute() with the independent and dependent data as the parameters.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
@@ -434,7 +715,7 @@ <h2>Training the model<a class="headerlink" href="#Training-the-model" title="Pe
 </div>
 </section>
 <section id="Prediction-(inference)-with-the-trained-model">
-<h2>Prediction (inference) with the trained model<a class="headerlink" href="#Prediction-(inference)-with-the-trained-model" title="Permalink to this heading"></a></h2>
+<h2>Prediction (inference) with the trained model<a class="headerlink" href="#Prediction-(inference)-with-the-trained-model" title="Link to this heading"></a></h2>
 <p>Now that the model is trained, we can test it with the test part of the dataset. We drop the same features to match that of the trained model, and put it into daal4py’s linear_regression_prediction class.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
@@ -474,7 +755,7 @@ <h2>Prediction (inference) with the trained model<a class="headerlink" href="#Pr
 <p>The graph above shows the Orange (predicted) result over the Blue (original data). This data is notoriously sparse in features leading to a difficult to predict target!</p>
 </section>
 <section id="Model-properties">
-<h2>Model properties<a class="headerlink" href="#Model-properties" title="Permalink to this heading"></a></h2>
+<h2>Model properties<a class="headerlink" href="#Model-properties" title="Link to this heading"></a></h2>
 <p>Another aspect of the model is the trained model’s properties, which are explored below.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
@@ -499,7 +780,7 @@ <h2>Model properties<a class="headerlink" href="#Model-properties" title="Permal
 </div>
 </section>
 <section id="Additional-metrics">
-<h2>Additional metrics<a class="headerlink" href="#Additional-metrics" title="Permalink to this heading"></a></h2>
+<h2>Additional metrics<a class="headerlink" href="#Additional-metrics" title="Link to this heading"></a></h2>
 <p>We can generate metrics on the independent data with daal4py’s low_order_moments() class.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
@@ -523,7 +804,7 @@ <h2>Additional metrics<a class="headerlink" href="#Additional-metrics" title="Pe
 </div>
 </section>
 <section id="Migrating-the-trained-model-for-inference-on-external-systems">
-<h2>Migrating the trained model for inference on external systems<a class="headerlink" href="#Migrating-the-trained-model-for-inference-on-external-systems" title="Permalink to this heading"></a></h2>
+<h2>Migrating the trained model for inference on external systems<a class="headerlink" href="#Migrating-the-trained-model-for-inference-on-external-systems" title="Link to this heading"></a></h2>
 <p>Occasionally one may need to migrate the trained model to another system for inference only–this use case allows the training on a much more powerful machine with a larger dataset, and placing the trained model for inference-only on a smaller machine.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
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@@ -608,6 +889,28 @@ <h2>Migrating the trained model for inference on external systems<a class="heade
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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diff --git a/latest/samples/dbscan.html b/latest/samples/dbscan.html
index 670282eb75..f2cee92c15 100644
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+
+
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@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
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           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
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+  <div class="version-switcher__container dropdown">
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+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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+</li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
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-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">DBSCAN for spoken arabic digit dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -110,30 +194,206 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
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+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
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-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
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@@ -160,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-DBSCAN-for-spoken-arabic-digit-dataset">
-<h1>Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-DBSCAN-for-spoken-arabic-digit-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-DBSCAN-for-spoken-arabic-digit-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -177,7 +437,7 @@ <h1>Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset<a
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -188,7 +448,7 @@ <h2>Download the data<a class="headerlink" href="#Download-the-data" title="Perm
 </div>
 </section>
 <section id="Preprocessing">
-<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to this heading"></a></h2>
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
 <p>Split the data into train and test sets</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -221,7 +481,7 @@ <h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to
 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
@@ -241,7 +501,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -289,7 +549,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 </div>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class DBSCAN</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
@@ -378,13 +638,13 @@ <h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+        <a href="tsne.html" class="btn btn-neutral float-right" title="Intel® Extension for Scikit-learn TSNE example" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+<jinja2.runtime.BlockReference object at 0x7f04b7395280>
 <a href=
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     Cookies
@@ -409,6 +669,28 @@ <h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>Davies
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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diff --git a/latest/samples/dbscan.ipynb b/latest/samples/dbscan.ipynb
index 829719629a..d6e5c92653 100644
--- a/latest/samples/dbscan.ipynb
+++ b/latest/samples/dbscan.ipynb
@@ -128,7 +128,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
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diff --git a/latest/samples/kmeans.html b/latest/samples/kmeans.html
index da7738fcc5..c64bc6bca8 100644
--- a/latest/samples/kmeans.html
+++ b/latest/samples/kmeans.html
@@ -1,26 +1,33 @@
+
+
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
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@@ -28,6 +35,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset" href="ridge_regression.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,42 +70,116 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
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+      <!-- dropdown will be populated by javascript on page load -->
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+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
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+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Kmeans for spoken arabic digit dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -109,31 +187,213 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -160,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-Kmeans-for-spoken-arabic-digit-dataset">
-<h1>Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Kmeans-for-spoken-arabic-digit-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Kmeans-for-spoken-arabic-digit-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -176,7 +436,7 @@ <h1>Intel® Extension for Scikit-learn Kmeans for spoken arabic digit dataset<a
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -187,7 +447,7 @@ <h2>Download the data<a class="headerlink" href="#Download-the-data" title="Perm
 </div>
 </section>
 <section id="Preprocessing">
-<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to this heading"></a></h2>
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
 <p>Split the data into train and test sets</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -230,7 +490,7 @@ <h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to
 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
@@ -250,7 +510,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the KMeans algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -302,7 +562,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 </div>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class KMeans</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
@@ -404,7 +664,7 @@ <h3>Compare inertia and number of iterations of patched Scikit-learn and origina
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57afd6c040>
+<jinja2.runtime.BlockReference object at 0x7f04b6868110>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -429,6 +689,28 @@ <h3>Compare inertia and number of iterations of patched Scikit-learn and origina
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/latest/samples/kmeans.ipynb b/latest/samples/kmeans.ipynb
index 8e6470f568..df09f8ded5 100644
--- a/latest/samples/kmeans.ipynb
+++ b/latest/samples/kmeans.ipynb
@@ -139,7 +139,7 @@
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    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/latest/samples/knn_mnist.html b/latest/samples/knn_mnist.html
index d311d80862..4b43e8e581 100644
--- a/latest/samples/knn_mnist.html
+++ b/latest/samples/knn_mnist.html
@@ -1,26 +1,33 @@
+
+
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-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Intel® Extension for Scikit-learn KNN for MNIST dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Intel® Extension for Scikit-learn KNN for MNIST dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/doctools.js?v=9bcbadda"></script>
+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +35,7 @@
     <link rel="prev" title="Samples" href="../samples.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,32 +70,56 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
@@ -99,40 +127,273 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -159,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-KNN-for-MNIST-dataset">
-<h1>Intel® Extension for Scikit-learn KNN for MNIST dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-KNN-for-MNIST-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn KNN for MNIST dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-KNN-for-MNIST-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -173,7 +434,7 @@ <h1>Intel® Extension for Scikit-learn KNN for MNIST dataset<a class="headerlink
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -203,7 +464,7 @@ <h2>Download the data<a class="headerlink" href="#Download-the-data" title="Perm
 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -223,7 +484,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training and predict KNN algorithm with Intel® Extension for Scikit-learn for MNIST dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -287,7 +548,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 <p><em>The first column of the classification report above is the class labels.</em></p>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class KNeighborsClassifier.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -396,7 +657,7 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57afd15430>
+<jinja2.runtime.BlockReference object at 0x7f04b5efd1c0>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -421,6 +682,28 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/latest/samples/knn_mnist.ipynb b/latest/samples/knn_mnist.ipynb
index 6b7e2465a0..b8604d70f0 100644
--- a/latest/samples/knn_mnist.ipynb
+++ b/latest/samples/knn_mnist.ipynb
@@ -104,7 +104,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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index 053a1522f4..7cb8de10d3 100644
--- a/latest/samples/lasso_regression.html
+++ b/latest/samples/lasso_regression.html
@@ -1,26 +1,33 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
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-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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-        <script src="../_static/sphinx_highlight.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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+      <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +35,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn ElasticNet for Airlines DepDelay dataset" href="ElasticNet.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,37 +70,82 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Lasso Regression for YearPredictionMSD dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Normalize-the-data">Normalize the data</a></li>
@@ -104,36 +153,247 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
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     </nav>
@@ -160,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-Lasso-Regression-for-YearPredictionMSD-dataset">
-<h1>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Lasso-Regression-for-YearPredictionMSD-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Lasso-Regression-for-YearPredictionMSD-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -180,7 +440,7 @@ <h1>Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD da
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
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@@ -225,7 +485,7 @@ <h2>Download the data<a class="headerlink" href="#Download-the-data" title="Perm
 </div>
 </section>
 <section id="Normalize-the-data">
-<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Permalink to this heading"></a></h2>
+<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
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 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
@@ -273,7 +533,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the Lasso algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -325,7 +585,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 </div>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class Lasso</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
@@ -421,7 +681,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
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     Cookies
@@ -446,6 +706,28 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
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diff --git a/latest/samples/lasso_regression.ipynb b/latest/samples/lasso_regression.ipynb
index 77571c2432..967d0d4e54 100644
--- a/latest/samples/lasso_regression.ipynb
+++ b/latest/samples/lasso_regression.ipynb
@@ -162,7 +162,7 @@
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    "metadata": {},
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-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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-  <title>Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +35,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Lasso Regression for YearPredictionMSD dataset" href="lasso_regression.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,38 +70,89 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
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+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Linear Regression for YearPredictionMSD dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Normalize-the-data">Normalize the data</a></li>
@@ -105,35 +160,240 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
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     </nav>
@@ -160,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-Linear-Regression-for-YearPredictionMSD-dataset">
-<h1>Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Linear-Regression-for-YearPredictionMSD-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Linear-Regression-for-YearPredictionMSD-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -180,7 +440,7 @@ <h1>Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD d
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -225,7 +485,7 @@ <h2>Download the data<a class="headerlink" href="#Download-the-data" title="Perm
 </div>
 </section>
 <section id="Normalize-the-data">
-<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Permalink to this heading"></a></h2>
+<h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
@@ -253,7 +513,7 @@ <h2>Normalize the data<a class="headerlink" href="#Normalize-the-data" title="Pe
 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
@@ -273,7 +533,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the Linear Regression algorithm with Intel® Extension for Scikit-learn for YearPredictionMSD dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -320,7 +580,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 </div>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class LinearRegression</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
@@ -416,7 +676,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0a90610>
+<jinja2.runtime.BlockReference object at 0x7f04b63eb950>
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     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -441,6 +701,28 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/latest/samples/linear_regression.ipynb b/latest/samples/linear_regression.ipynb
index 4a356e6bfc..508ee06d8c 100644
--- a/latest/samples/linear_regression.ipynb
+++ b/latest/samples/linear_regression.ipynb
@@ -162,7 +162,7 @@
    "id": "20c5ab48",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/latest/samples/logistictic_regression_cifar.html b/latest/samples/logistictic_regression_cifar.html
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+++ b/latest/samples/logistictic_regression_cifar.html
@@ -1,26 +1,33 @@
+
+
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+<html class="writer-html5" lang="en" data-content_root="../">
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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+  <title>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
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+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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@@ -28,6 +35,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn KNN for MNIST dataset" href="knn_mnist.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
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+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
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@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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@@ -66,73 +70,330 @@
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 </div>
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+  <div class="version-switcher__container dropdown">
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+      <!-- dropdown will be populated by javascript on page load -->
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+  
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
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-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
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-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Logistic Regression for Cifar dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
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-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
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@@ -159,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-Logistic-Regression-for-Cifar-dataset">
-<h1>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Logistic-Regression-for-Cifar-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Logistic-Regression-for-Cifar-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -175,7 +436,7 @@ <h1>Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset<a c
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -207,7 +468,7 @@ <h2>Download the data<a class="headerlink" href="#Download-the-data" title="Perm
 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -227,7 +488,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the Logistic Regression algorithm with Intel® Extension for Scikit-learn for CIFAR dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -279,7 +540,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 </div>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class LogisticRegression</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -375,7 +636,7 @@ <h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss me
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0b172b0>
+<jinja2.runtime.BlockReference object at 0x7f04f038a840>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -400,6 +661,28 @@ <h3>Compare Log Loss metric of patched Scikit-learn and original</h3>Log Loss me
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/latest/samples/logistictic_regression_cifar.ipynb b/latest/samples/logistictic_regression_cifar.ipynb
index f4cb2b95a7..43727804d7 100644
--- a/latest/samples/logistictic_regression_cifar.ipynb
+++ b/latest/samples/logistictic_regression_cifar.ipynb
@@ -108,7 +108,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
   },
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diff --git a/latest/samples/nusvr_medical_charges.html b/latest/samples/nusvr_medical_charges.html
index fcebb94a1c..82c8dc7a8c 100644
--- a/latest/samples/nusvr_medical_charges.html
+++ b/latest/samples/nusvr_medical_charges.html
@@ -1,26 +1,33 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
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-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+      <link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
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-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="../_static/documentation_options.js?v=aa5d0537"></script>
+      <script src="../_static/doctools.js?v=9bcbadda"></script>
+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +35,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Linear Regression for YearPredictionMSD dataset" href="linear_regression.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,39 +70,96 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -106,34 +167,233 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -160,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-NuSVR-for-Medical-Charges-dataset">
-<h1>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-NuSVR-for-Medical-Charges-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-NuSVR-for-Medical-Charges-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -176,7 +436,7 @@ <h1>Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset<a class
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -187,7 +447,7 @@ <h2>Download the data<a class="headerlink" href="#Download-the-data" title="Perm
 </div>
 </section>
 <section id="Preprocessing">
-<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to this heading"></a></h2>
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
 <p>Encode categorical features</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -219,7 +479,7 @@ <h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to
 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -239,7 +499,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
@@ -290,7 +550,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 </div>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class NuSVR</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
@@ -385,7 +645,7 @@ <h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patche
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0c53220>
+<jinja2.runtime.BlockReference object at 0x7f04b66c88f0>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -410,6 +670,28 @@ <h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patche
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/latest/samples/nusvr_medical_charges.ipynb b/latest/samples/nusvr_medical_charges.ipynb
index bcfbe9154f..8c72c1b71d 100644
--- a/latest/samples/nusvr_medical_charges.ipynb
+++ b/latest/samples/nusvr_medical_charges.ipynb
@@ -133,7 +133,7 @@
    "id": "6bb14ac8",
    "metadata": {},
    "source": [
-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
    ]
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diff --git a/latest/samples/random_forest_yolanda.html b/latest/samples/random_forest_yolanda.html
index b50f5ee8ab..cd4b96f9bb 100644
--- a/latest/samples/random_forest_yolanda.html
+++ b/latest/samples/random_forest_yolanda.html
@@ -1,26 +1,33 @@
+
+
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+<html class="writer-html5" lang="en" data-content_root="../">
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-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
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-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="shortcut icon" href="../_static/favicons.png"/>
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+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="../_static/documentation_options.js?v=aa5d0537"></script>
+      <script src="../_static/doctools.js?v=9bcbadda"></script>
+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script src="../_static/version_switcher.js?v=107152b5"></script>
+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -28,6 +35,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset" href="nusvr_medical_charges.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,73 +70,330 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Random Forest for Yolanda dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -159,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-Random-Forest-for-Yolanda-dataset">
-<h1>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Random-Forest-for-Yolanda-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Random-Forest-for-Yolanda-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -173,7 +434,7 @@ <h1>Intel® Extension for Scikit-learn Random Forest for Yolanda dataset<a class
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
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 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -223,7 +484,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -270,7 +531,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 </div>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class RandomForestRegressor.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -366,7 +627,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0c8bdc0>
+<jinja2.runtime.BlockReference object at 0x7f04b66e84d0>
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     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -391,6 +652,28 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/latest/samples/random_forest_yolanda.ipynb b/latest/samples/random_forest_yolanda.ipynb
index a184b22fd1..276284ed9b 100644
--- a/latest/samples/random_forest_yolanda.ipynb
+++ b/latest/samples/random_forest_yolanda.ipynb
@@ -104,7 +104,7 @@
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    "metadata": {},
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-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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diff --git a/latest/samples/ridge_regression.html b/latest/samples/ridge_regression.html
index 4792f70a14..810d18e34e 100644
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+
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset &mdash; Intel(R) Extension for Scikit-learn* 2025.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+      <script src="../_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
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@@ -28,6 +35,7 @@
     <link rel="prev" title="Intel® Extension for Scikit-learn Random Forest for Yolanda dataset" href="random_forest_yolanda.html" />
     <script defer type="text/javascript" src="https://www.intel.com/content/dam/www/global/wap/performance-config.js" ></script>
     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
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@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -66,41 +70,109 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
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+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+      <!-- dropdown will be populated by javascript on page load -->
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+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Rigde Regression for Airlines DepDelay dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Preprocessing">Preprocessing</a></li>
@@ -108,32 +180,220 @@
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -160,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-Ridge-Regression-for-Airlines-DepDelay-dataset">
-<h1>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Ridge-Regression-for-Airlines-DepDelay-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-Ridge-Regression-for-Airlines-DepDelay-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -178,7 +438,7 @@ <h1>Intel® Extension for Scikit-learn Ridge Regression for Airlines DepDelay da
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -189,7 +449,7 @@ <h2>Download the data<a class="headerlink" href="#Download-the-data" title="Perm
 </div>
 </section>
 <section id="Preprocessing">
-<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to this heading"></a></h2>
+<h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Link to this heading"></a></h2>
 <p>Let’s encode categorical features with LabelEncoder</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -251,7 +511,7 @@ <h2>Preprocessing<a class="headerlink" href="#Preprocessing" title="Permalink to
 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -271,7 +531,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
 </pre></div></div>
 </div>
-<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
+<p>Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the <a class="reference external" href="https://intel.github.io/scikit-learn-intelex/latest/algorithms.html">list of supported algorithms and parameters</a> for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">submit an issue on GitHub</a>.</p>
 <p>Training of the Ridge Regression algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
@@ -323,7 +583,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 </div>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class Ridge</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
@@ -419,7 +679,7 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
   <hr/>
 
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     Cookies
@@ -444,6 +704,28 @@ <h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of pa
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
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diff --git a/latest/samples/ridge_regression.ipynb b/latest/samples/ridge_regression.ipynb
index c3d0856d00..1c159a13ae 100644
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+++ b/latest/samples/ridge_regression.ipynb
@@ -169,7 +169,7 @@
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    "metadata": {},
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-    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
+    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/intel/scikit-learn-intelex/issues)."
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-  <title>Intel® Extension for Scikit-learn SVC for Adult dataset &mdash; Intel(R) Extension for Scikit-learn* 2023.2 documentation</title>
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+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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@@ -28,6 +35,7 @@
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@@ -54,10 +62,6 @@
           <a href="../index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
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-              <div class="version">
-                <a href="../../versions.html">2023.2 ▼</a>
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../support.html">Support</a></li>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#unpatching">Unpatching</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
-<ul>
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+</li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../quick-start.html#memory-requirements">Memory Requirements</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul class="current">
+</li>
 <li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Support Vector Classification (SVC) for Adult dataset</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Download-the-data">Download the data</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
 <li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
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+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
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-<p class="caption" role="heading"><span class="caption-text">License</span></p>
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+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
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+
         </div>
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     </nav>
@@ -159,7 +420,7 @@
            <div itemprop="articleBody">
              
   <section id="Intel®-Extension-for-Scikit-learn-SVC-for-Adult-dataset">
-<h1>Intel® Extension for Scikit-learn SVC for Adult dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-SVC-for-Adult-dataset" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn SVC for Adult dataset<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-SVC-for-Adult-dataset" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
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@@ -173,7 +434,7 @@ <h1>Intel® Extension for Scikit-learn SVC for Adult dataset<a class="headerlink
 </div>
 </div>
 <section id="Download-the-data">
-<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Permalink to this heading"></a></h2>
+<h2>Download the data<a class="headerlink" href="#Download-the-data" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
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 </div>
 </section>
 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -270,7 +531,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 <p><em>The first column of the classification report above is the class labels.</em></p>
 </section>
 <section id="Train-the-same-algorithm-with-original-Scikit-learn">
-<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Train the same algorithm with original Scikit-learn<a class="headerlink" href="#Train-the-same-algorithm-with-original-Scikit-learn" title="Link to this heading"></a></h2>
 <p>In order to cancel optimizations, we use <em>unpatch_sklearn</em> and reimport the class SVC.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
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   <hr/>
 
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@@ -396,6 +657,28 @@ <h2>With scikit-learn-intelex patching you can:</h2><ul><li>Use your Scikit-lear
       </div>
     </section>
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+  <div class="version-switcher__container dropdown">
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+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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diff --git a/latest/samples/tsne.html b/latest/samples/tsne.html
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+++ b/latest/samples/tsne.html
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+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../samples.html">Samples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../kaggle/automl.html">AutoML Workflows</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../distributed-mode.html">Distributed Mode</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../deprecation.html">Deprecation Notice</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
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-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../kaggle.html">Kaggle Kernels</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="../non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../guide/acceleration.html#tsne">TSNE</a></li>
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   <section id="Intel®-Extension-for-Scikit-learn-TSNE-example">
-<h1>Intel® Extension for Scikit-learn TSNE example<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-TSNE-example" title="Permalink to this heading"></a></h1>
+<h1>Intel® Extension for Scikit-learn TSNE example<a class="headerlink" href="#Intel®-Extension-for-Scikit-learn-TSNE-example" title="Link to this heading"></a></h1>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
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@@ -155,7 +439,7 @@ <h1>Intel® Extension for Scikit-learn TSNE example<a class="headerlink" href="#
 </div>
 </div>
 <section id="Generate-the-data">
-<h2>Generate the data<a class="headerlink" href="#Generate-the-data" title="Permalink to this heading"></a></h2>
+<h2>Generate the data<a class="headerlink" href="#Generate-the-data" title="Link to this heading"></a></h2>
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 <div class="line">Generate isotropic Gaussian blobs for clustering. With the number of samples: 20k Number of features: 100 Number of blobs: 4 Source:</div>
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@@ -170,7 +454,7 @@ <h2>Generate the data<a class="headerlink" href="#Generate-the-data" title="Perm
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 <section id="Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">
-<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Permalink to this heading"></a></h2>
+<h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class="headerlink" href="#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn" title="Link to this heading"></a></h2>
 <p>Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
@@ -258,7 +542,7 @@ <h2>Patch original Scikit-learn with Intel® Extension for Scikit-learn<a class=
 </div>
 </section>
 <section id="Plot-embeddings-original-scikit-learn-and-Intel®-extension">
-<h2>Plot embeddings original scikit-learn and Intel® extension<a class="headerlink" href="#Plot-embeddings-original-scikit-learn-and-Intel®-extension" title="Permalink to this heading"></a></h2>
+<h2>Plot embeddings original scikit-learn and Intel® extension<a class="headerlink" href="#Plot-embeddings-original-scikit-learn-and-Intel®-extension" title="Link to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
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 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
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 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
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diff --git a/latest/support.html b/latest/support.html
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--- a/latest/support.html
+++ b/latest/support.html
@@ -1,30 +1,38 @@
+
+
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               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#issues">Issues</a></li>
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-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
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+  <section id="intel-r-extension-for-scikit-learn-support">
+<h1>Intel(R) Extension for Scikit-learn Support<a class="headerlink" href="#intel-r-extension-for-scikit-learn-support" title="Link to this heading"></a></h1>
+<p>We are committed to providing support and assistance to help you make the most out of Intel(R) Extension for Scikit-learn.</p>
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+<h2>Issues<a class="headerlink" href="#issues" title="Link to this heading"></a></h2>
+<p>If you have a problem, check out the <a class="reference external" href="https://github.com/intel/scikit-learn-intelex/issues">GitHub Issues</a> to see if the issue you want to address is already reported.</p>
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+              <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="verbose.html">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Performance</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
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+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorials &amp; Case Studies</a><ul>
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+  <section id="intelex-tutorials-case-studies">
+<h1>Intel® Extension for Scikit-learn* Tutorials &amp; Case Studies<a class="headerlink" href="#intelex-tutorials-case-studies" title="Link to this heading"></a></h1>
+<p>Explore the tutorials &amp; case studies to help you to harness the full potential of Intel® Extension for Scikit-learn*.</p>
+<section id="tutorials">
+<h2>Tutorials<a class="headerlink" href="#tutorials" title="Link to this heading"></a></h2>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
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+<div class="sd-card-title sd-font-weight-bold docutils">
+Introduction to scikit-learn* Essentials for Machine Learning</div>
+<p class="sd-card-text">An overview of scikit-learn essentials.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/introduction-to-scikit-learn-essentials-for-ml.html"><span>https://www.intel.com/content/www/us/en/developer/videos/introduction-to-scikit-learn-essentials-for-ml.html</span></a></div>
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+<div class="sd-card-title sd-font-weight-bold docutils">
+Advanced scikit-learn* Essentials for Machine Learning</div>
+<p class="sd-card-text">Special technique to perform scikit-learn computation on Intel GPUs.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/advanced-scikit-learn-essentials-for-ml.html"><span>https://www.intel.com/content/www/us/en/developer/videos/advanced-scikit-learn-essentials-for-ml.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Develop Efficient AI Solutions with Accelerated Machine Learning</div>
+<p class="sd-card-text">Techniques for maximizing Intel® Extension for Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/accelerated-machine-learning-for-ai-solutions.html"><span>https://www.intel.com/content/www/us/en/developer/videos/accelerated-machine-learning-for-ai-solutions.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Getting started with classical Machine Learning Frameworks using Google Colaboratory</div>
+<p class="sd-card-text">Simple Installation of Intel® Extension for Scikit-learn* on Google Colaboratory.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Getting-started-with-classical-Machine-Learning-Frameworks-using/post/1450139"><span>https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Getting-started-with-classical-Machine-Learning-Frameworks-using/post/1450139</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Accelerate Machine Learning Workloads: K-means and GPairs Algorithms</div>
+<p class="sd-card-text">An introduction to Intel® Extension for Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/accelerate-ml-workloads-k-means-gpairs-algorithms.html"><span>https://www.intel.com/content/www/us/en/developer/videos/accelerate-ml-workloads-k-means-gpairs-algorithms.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Benchmarking Intel® Extension for Scikit-learn*: How Much Faster Is It?</div>
+<p class="sd-card-text">Explore and compare the performance of the Intel Extension for Scikit-learn* vs. stock Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/technical/benchmarking-intel-extension-for-scikit-learn.html"><span>https://www.intel.com/content/www/us/en/developer/articles/technical/benchmarking-intel-extension-for-scikit-learn.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Drive Innovation &amp; Performance into Your scikit-learn* Machine Learning Tasks</div>
+<p class="sd-card-text">An overview of scikit-learn essentials.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/videos/drive-innovation-performance-scikit-learn-ml-tasks.html"><span>https://www.intel.com/content/www/us/en/developer/videos/drive-innovation-performance-scikit-learn-ml-tasks.html</span></a></div>
+</div>
+</div>
+</div>
+</section>
+<section id="case-studies">
+<h2>Case Studies<a class="headerlink" href="#case-studies" title="Link to this heading"></a></h2>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
+<div class="sd-row sd-row-cols-3 sd-row-cols-xs-3 sd-row-cols-sm-3 sd-row-cols-md-3 sd-row-cols-lg-3 docutils">
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Greener Machine Learning Computing with Intel® AI Acceleration</div>
+<p class="sd-card-text">Lowering the Environmental Impact of AI Workloads by 7x using Intel® Extension for Scikit-learn*.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/case-study/greener-machine-learning-with-intel-ai.html"><span>https://www.intel.com/content/www/us/en/developer/articles/case-study/greener-machine-learning-with-intel-ai.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+Supply Chain Optimization at Enterprise Scale</div>
+<p class="sd-card-text">Leveraging Open-Source AI Technologies with Red Hat OpenShift* Data Science and Intel® Architecture.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/technical/supply-chain-optimization-at-enterprise-scale.html"><span>https://www.intel.com/content/www/us/en/developer/articles/technical/supply-chain-optimization-at-enterprise-scale.html</span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row sd-p-1 docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+HippoScreen Improves AI Performance by 2.4x with oneAPI Tools</div>
+<p class="sd-card-text">The neurotechnology startup used Intel® Extension for Scikit-learn* to the improve efficiency and build times of deep-learning models used in its Brain Waves AI system.</p>
+</div>
+<a class="sd-stretched-link sd-hide-link-text reference external" href="https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html"><span>https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html</span></a></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="guide/acceleration.html" class="btn btn-neutral float-left" title="Tuning Guide" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+      Choose version  <!-- this text may get changed later by javascript -->
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diff --git a/latest/verbose.html b/latest/verbose.html
index 091c3521e8..ca5042ca9b 100644
--- a/latest/verbose.html
+++ b/latest/verbose.html
@@ -1,30 +1,38 @@
+
+
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-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
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-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
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+    <link rel="shortcut icon" href="_static/favicons.png"/>
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
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+      <script src="_static/design-tabs.js?v=f930bc37"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-    <link rel="next" title="Release Notes" href="releases.html" />
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+    <link rel="next" title="Preview Functionality" href="preview.html" />
+    <link rel="prev" title="Array API support" href="array_api.html" />
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     <script type="text/javascript">
+        fetch("/scikit-learn-intelex/versions.json").then(response => response.text()).then(respText=> load_versions(respText));
         // Configure TMS settings
         var wapLocalCode = 'us-en'; // Dynamically set per localized site, see mapping table for values
         var wapSection = "scikit-learn"; // WAP team will give you a unique section for your site
@@ -51,10 +59,6 @@
           <a href="index.html" class="icon icon-home">
             Intel(R) Extension for Scikit-learn*
           </a>
-              <div class="version">
-                <a href="../versions.html">2023.2 ▼</a>
-                <p>Click link above to switch version</p>
-              </div>
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     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -63,53 +67,330 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
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+    <div id="version-switcher-button"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+  
+  
               <p class="caption" role="heading"><span class="caption-text">Get Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a></li>
-<li class="toctree-l1"><a class="reference internal" href="what-is-patching.html">Patching</a></li>
-<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quick-start.html">Quick Start</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#compatibility-with-scikit-learn">Compatibility with Scikit-learn*</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#integrate-intelex">Integrate Intel® Extension for Scikit-learn*</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#patching">Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#global-patching">Global Patching</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#unpatching">Unpatching</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#installation">Installation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-pypi">Install from PyPI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-from-anaconda-cloud">Install from Anaconda* Cloud</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#build-from-sources">Build from Sources</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#install-intel-r-ai-tools">Install Intel*(R) AI Tools</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#release-notes">Release Notes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="quick-start.html#system-requirements">System Requirements</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#hardware-requirements">Hardware Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#software-requirements">Software Requirements</a></li>
+<li class="toctree-l3"><a class="reference internal" href="quick-start.html#memory-requirements">Memory Requirements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="samples/knn_mnist.html">k-Nearest Neighbors (kNN) for MNIST dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/knn_mnist.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/logistictic_regression_cifar.html">Logistic Regression for Cifar dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/logistictic_regression_cifar.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/svc_adult.html">Support Vector Classification (SVC) for Adult dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/svc_adult.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ElasticNet.html">ElasticNet for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ElasticNet.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/lasso_regression.html">Lasso Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/lasso_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/linear_regression.html">Linear Regression for YearPredictionMSD dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Normalize-the-data">Normalize the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/linear_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/nusvr_medical_charges.html">Nu-Support Vector Regression (NuSVR) for Medical Charges dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/nusvr_medical_charges.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/random_forest_yolanda.html">Random Forest for Yolanda dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/random_forest_yolanda.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/ridge_regression.html">Rigde Regression for Airlines DepDelay dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/ridge_regression.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/kmeans.html">Kmeans for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/kmeans.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/dbscan.html">DBSCAN for spoken arabic digit dataset</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Download-the-data">Download the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Preprocessing">Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/dbscan.html#Train-the-same-algorithm-with-original-Scikit-learn">Train the same algorithm with original Scikit-learn</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="samples/tsne.html">Intel® Extension for Scikit-learn TSNE example</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Generate-the-data">Generate the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Patch-original-Scikit-learn-with-Intel®-Extension-for-Scikit-learn">Patch original Scikit-learn with Intel® Extension for Scikit-learn</a></li>
+<li class="toctree-l3"><a class="reference internal" href="samples/tsne.html#Plot-embeddings-original-scikit-learn-and-Intel®-extension">Plot embeddings original scikit-learn and Intel® extension</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted">Intel® Extension for Scikit-Learn* Getting Started Sample</a></li>
+<li class="toctree-l2"><a class="reference external" href="https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult">Intel® Extension for Scikit-Learn*: SVC for Adult dataset Performance Sample</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#acceleration">Acceleration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kaggle.html#machine-learning-workflows">Machine Learning Workflows</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-scikit-learn-and-intelex">Kaggle kernels that use scikit-learn and Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/classification.html">Classification Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#binary-classification">Binary Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#multiclass-classification">MultiClass Classification</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-computer-vision">Classification Tasks in Computer Vision</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/classification.html#classification-tasks-in-natural-language-processing">Classification Tasks in Natural Language Processing</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/regression.html">Regression Tasks</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#using-a-single-regressor">Using a Single Regressor</a></li>
+<li class="toctree-l5"><a class="reference internal" href="kaggle/regression.html#stacking-regressors">Stacking Regressors</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kaggle.html#kaggle-kernels-that-use-automl-with-intelex">Kaggle kernels that use AutoML with Intel® Extension for Scikit-learn*:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kaggle/automl.html">AutoML Workflows</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Features</span></p>
+<p class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a></li>
+<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-cpu">on CPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#classification">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#regression">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#clustering">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#dimensionality-reduction">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#nearest-neighbors">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#other-tasks">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#on-gpu">on GPU</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id1">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id2">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id3">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id4">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id5">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id6">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#spmd-support">SPMD Support</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id7">Classification</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id8">Regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id9">Clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id10">Dimensionality Reduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id11">Nearest Neighbors</a></li>
+<li class="toctree-l3"><a class="reference internal" href="algorithms.html#id12">Other Tasks</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="algorithms.html#scikit-learn-tests">Scikit-learn Tests</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="oneapi-gpu.html">oneAPI and GPU support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#device-offloading">Device offloading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="oneapi-gpu.html#example">Example</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="distributed-mode.html">Distributed Mode</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="non-scikit-algorithms.html">Non-Scikit-Learn Algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#basicstatistics">BasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics"><code class="docutils literal notranslate"><span class="pre">BasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.second_order_raw_moment_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Releases</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.BasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">BasicStatistics.fit()</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Installation Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="system-requirements.html">System Requirements</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory-requirements.html">Memory Requirements</a></li>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalbasicstatistics">IncrementalBasicStatistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.min_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.min_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.max_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.max_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.mean_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.mean_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variance_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.variation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.variation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.standard_deviation_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.standard_deviation_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.sum_squares_centered_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.sum_squares_centered_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.second_order_raw_moment_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.second_order_raw_moment_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.n_features_in_</span></code></a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="samples.html">Samples</a></li>
-<li class="toctree-l1"><a class="reference internal" href="kaggle.html">Kaggle Kernels</a></li>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.basic_statistics.IncrementalBasicStatistics.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalBasicStatistics.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementalempiricalcovariance">IncrementalEmpiricalCovariance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.location_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.location_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.covariance_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.covariance_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.covariance.IncrementalEmpiricalCovariance.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalEmpiricalCovariance.partial_fit()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="non-scikit-algorithms.html#incrementallinearregression">IncrementalLinearRegression</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.coef_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.coef_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.intercept_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.intercept_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_samples_seen_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_samples_seen_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.batch_size_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.batch_size_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.n_features_in_"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.n_features_in_</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.partial_fit"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.partial_fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="non-scikit-algorithms.html#sklearnex.linear_model.IncrementalLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">IncrementalLinearRegression.predict()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="input-types.html">Supported input types</a></li>
+<li class="toctree-l1"><a class="reference internal" href="array_api.html">Array API support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-dpnp-and-dpctl">Support for DPNP and DPCTL</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#support-for-array-api-compatible-inputs">Support for Array API-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="array_api.html#example-usage">Example usage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpnp-ndarrays">DPNP ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#dpctl-usm-ndarrays">DPCTL usm_ndarrays</a></li>
+<li class="toctree-l3"><a class="reference internal" href="array_api.html#use-of-array-api-strict">Use of <cite>array-api-strict</cite></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Verbose Mode</a></li>
+<li class="toctree-l1"><a class="reference internal" href="preview.html">Preview Functionality</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deprecation.html">Deprecation Notice</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="deprecation.html#macos-support">macOS* Support</a></li>
+</ul>
+</li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Performance</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="acceleration.html">Acceleration</a></li>
-<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Performance Tuning Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guide/acceleration.html">Tuning Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#tsne">TSNE</a></li>
+<li class="toctree-l2"><a class="reference internal" href="guide/acceleration.html#random-forest">Random Forest</a></li>
+</ul>
+</li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">Blogs &amp; Publications</span></p>
+<p class="caption" role="heading"><span class="caption-text">Learn</span></p>
 <ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials &amp; Case Studies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#tutorials">Tutorials</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials.html#case-studies">Case Studies</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="blogs.html">Medium Blogs</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">How to contribute</span></p>
+<p class="caption" role="heading"><span class="caption-text">More</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a></li>
+<li class="toctree-l1"><a class="reference internal" href="support.html">Support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="support.html#issues">Issues</a></li>
 </ul>
-<p class="caption" role="heading"><span class="caption-text">License</span></p>
-<ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="contribute.html">How to Contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#licensing">Licensing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#pull-requests">Pull Requests</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="contribute.html#before-contributing-changes">Before Contributing Changes</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="contribute.html#code-style">Code Style</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
 </ul>
 
+
         </div>
       </div>
     </nav>
@@ -135,7 +416,7 @@
            <div itemprop="articleBody">
              
   <section id="verbose-mode">
-<span id="verbose"></span><h1>Verbose Mode<a class="headerlink" href="#verbose-mode" title="Permalink to this heading"></a></h1>
+<span id="verbose"></span><h1>Verbose Mode<a class="headerlink" href="#verbose-mode" title="Link to this heading"></a></h1>
 <p>Use Intel® Extension for Scikit-learn* in verbose mode to find out which implementation of the algorithm is currently used,
 Intel® Extension for Scikit-learn* or original Scikit-learn.</p>
 <div class="admonition note">
@@ -144,12 +425,12 @@
 </div>
 <p>To enable verbose mode, set the <code class="docutils literal notranslate"><span class="pre">SKLEARNEX_VERBOSE</span></code> environment variable as shown below:</p>
 <ul>
-<li><p>On Linux and MacOS:</p>
+<li><p>On Linux* OS</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
 </pre></div>
 </div>
 </li>
-<li><p>On Windows:</p>
+<li><p>On Windows* OS</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span> <span class="n">SKLEARNEX_VERBOSE</span><span class="o">=</span><span class="n">INFO</span>
 </pre></div>
 </div>
@@ -177,14 +458,14 @@
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="distributed-mode.html" class="btn btn-neutral float-left" title="Distributed Mode" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="releases.html" class="btn btn-neutral float-right" title="Release Notes" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="array_api.html" class="btn btn-neutral float-left" title="Array API support" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="preview.html" class="btn btn-neutral float-right" title="Preview Functionality" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f57b0c8b6a0>
+<jinja2.runtime.BlockReference object at 0x7f04b63540e0>
 <a href=
     "https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" data-cookie-notice="true">
     Cookies
@@ -209,6 +490,28 @@
       </div>
     </section>
   </div>
+  <div class="version-switcher__container dropdown">
+    <script>
+      current_version = "2025.1"
+      project_name = "scikit-learn-intelex"
+    </script>
+    <div id="version-switcher-button"
+      class="version-switcher__button"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="version-switcher-dropdown"
+      aria-label="Version switcher list"
+      style="display:inline-block;padding-left:10px;padding-right:10px;"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </div>
+    <div id="version-switcher-dropdown"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="">
+      <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
   <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
diff --git a/versions.html b/versions.html
deleted file mode 100644
index a0a9cc1ddf..0000000000
--- a/versions.html
+++ /dev/null
@@ -1,102 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
-<head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
-  <meta content="This website introduces Intel® Extension for Scikit-learn*" name="description" />
-  <meta content="Intel optimization, Scikit-learn, Intel® Extension for Scikit-learn*, GPU, discrete GPU, Intel discrete GPU" name="keywords" />
-
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Welcome to Intel® Extension for Scikit-learn* Documentation</title>
-  <link rel="stylesheet" href="_css/theme.css" type="text/css" />
-  <link rel="stylesheet" href="_css/custom.css" type="text/css" />
-  <script src="_scripts/jquery.js"></script>
-  <script src="_scripts/theme.js"></script>
-</head>
-
-<body class="wy-body-for-nav">
-  <div class="wy-grid-for-nav">
-    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
-      <div class="wy-side-scroll">
-        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-          <p id="version-pick"><span><b>Pick a version:</b></span></p>
-          <!-- <p><span><b>CPU</b></span></p> -->
-          <ul id="ul_cpu">
-            <li class="toctree-l1"><a class="reference internal" href="latest">latest</a></li>
-            <li class="toctree-l1"><a class="reference internal" href="2023.2">v2023.2</a></li>
-          </ul>
-          <!-- <p class="menu-separator"></p>
-          <p<span><b>XPU/GPU</b></span></p>
-          <ul id="ul_gpu">
-          </ul> -->
-        </div>
-      </div>
-    </nav>
-
-    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
-      <nav class="wy-nav-top" aria-label="Mobile navigation menu" >
-        <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-        <a href="#">intel_extension_for_scikit_learn</a>
-      </nav>
-
-      <div class="wy-nav-content">
-        <div class="rst-content">
-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-            <div itemprop="articleBody" >
-              <section id="welcome-to-intel-extension-for-scikit-learn-documentation">
-                <h1>Welcome to Intel® Extension for Scikit-learn* Documentation<a class="headerlink" href="#welcome-to-intel-extension-for-scikit-learn-documentation" title="Permalink to this heading"></a></h1>
-                <p>Scikit-learn* is a Python* module for machine learning. Intel® Extension for Scikit-learn* seamlessly speeds up your scikit-learn applications for Intel CPUs and GPUs across single- and multi-node configurations. This extension package dynamically patches scikit-learn estimators while improving performance for your machine learning algorithms.</p>
-                <p>The extension is part of the Intel® AI Analytics Toolkit (AI Kit) that provides flexibility to use machine learning tools with your existing AI packages.</p>
-                <p>Using scikit-learn with this extension, you can:</p>
-                  <ul>
-                    <li>Speed up training and inference by up to 100x with the equivalent mathematical accuracy.</li>
-                    <li>Continue to use the open source scikit-learn API.</li>
-                    <li>Enable and disable the extension with a couple lines of code or at the command line.</li>
-                  </ul>
-                <p>You can learn more about Intel® Extension for Scikit-Learn* in the following <a href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html">video</a>.</p>
-                <p><b>Designed for Data Scientists and Framework Designers</b></p>
-                <p>Intel® Extension for Scikit-learn* was created to provide data scientists with a way to get a better performance while using the familiar scikit-learn package and getting the same results.</p>
-                <p>To get started with Intel® Extension for Scikit-Learn*, please review the <a href="latest/gsg.html">Getting Started Guide</a> and <a href="latest/quick-start.html">Quick Start Guide</a> sections of the documentation.</p>
-                <!-- <p>Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel X<sup>e</sup> Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* <cite>xpu</cite> device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*.</p>
-                <p>Intel® Extension for PyTorch* provides optimizations for both eager mode and graph mode, however, compared to eager mode, graph mode in PyTorch* normally yields better performance from optimization techniques, such as operation fusion. Intel® Extension for PyTorch* amplifies them with more comprehensive graph optimizations. Therefore we recommend you to take advantage of Intel® Extension for PyTorch* with <a class="reference external" href="https://pytorch.org/docs/stable/jit.html">TorchScript</a> whenever your workload supports it. You could choose to run with <cite>torch.jit.trace()</cite> function or <cite>torch.jit.script()</cite> function, but based on our evaluation, <cite>torch.jit.trace()</cite> supports more workloads so we recommend you to use <cite>torch.jit.trace()</cite> as your first choice.</p>
-                <p>The extension can be loaded as a Python module for Python programs or linked as a C++ library for C++ programs. In Python scripts users can enable it dynamically by importing <cite>intel_extension_for_pytorch</cite>.</p>
-                <p>Intel® Extension for PyTorch* is structured as shown in the following figure:</p>
-                <figure class="align-center">
-                  <a class="reference internal image-reference" href="_images/intel_extension_for_pytorch_structure.png"><img alt="Architecture of Intel® Extension for PyTorch*" src="_images/intel_extension_for_pytorch_structure.png" style="width: 800px;" /></a>
-                </figure>
-                <div class="line-block">
-                  <div class="line"><br /></div>
-                </div>
-                <p>Optimizations for both eager mode and graph mode contribute to extra performance accelerations with the extension. In eager mode, the PyTorch frontend is extended with custom Python modules (such as fusion modules), optimal optimizers, and INT8 quantization APIs. Further performance boost is available by converting the eager-mode model into graph mode via extended graph fusion passes. In the graph mode, the fusions reduce operator/kernel invocation overheads, and thus increase performance. On CPU, Intel® Extension for PyTorch* dispatches the operators into their underlying kernels automatically based on ISA that it detects and leverages vectorization and matrix acceleration units available on Intel hardware. Intel® Extension for PyTorch* runtime extension brings better efficiency with finer-grained thread runtime control and weight sharing. On GPU, optimized operators and kernels are implemented and registered through PyTorch dispatching mechanism. These operators and kernels are accelerated from native vectorization feature and matrix calculation feature of Intel GPU hardware. Intel® Extension for PyTorch* for GPU utilizes the <a class="reference external" href="https://github.com/intel/llvm#oneapi-dpc-compiler">DPC++</a> compiler that supports the latest <a class="reference external" href="https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html">SYCL*</a> standard and also a number of extensions to the SYCL* standard, which can be found in the <a class="reference external" href="https://github.com/intel/llvm/tree/sycl/sycl/doc/extensions">sycl/doc/extensions</a> directory.</p>
-                <div class="admonition note">
-                  <p class="admonition-title">Note</p>
-                  <p>GPU features are not included in CPU only packages.</p>
-                </div> 
-                <p>Intel® Extension for PyTorch* has been released as an open–source project at <a class="reference external" href="https://github.com/intel/intel-extension-for-pytorch">Github</a>. Source code is available at <a class="reference external" href="https://github.com/intel/intel-extension-for-pytorch/tree/xpu-master">xpu-master branch</a>. Check <a class="reference internal" href="./xpu/latest/">the tutorial</a> for detailed information. Due to different development schedule, optimizations for CPU only might have a newer code base. Source code is available at <a class="reference external" href="https://github.com/intel/intel-extension-for-pytorch/tree/master">master branch</a>. Check <a class="reference internal" href="./cpu/latest/">the CPU tutorial</a> for detailed information on the CPU side.</p>
-                -->
-                <div class="toctree-wrapper compound">
-                </div> 
-              </section> 
-            </div>
-          </div>
-
-          <footer>
-            <hr/>
-            <div role="contentinfo">
-              <p>&#169; Copyright Intel(R).</p>
-            </div>
-            Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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diff --git a/versions.json b/versions.json
index 4b24b8238d..3a48fb7e81 100644
--- a/versions.json
+++ b/versions.json
@@ -1,15 +1,33 @@
 [
     {
-        "name": "2024.6 (latest)",
+        "name": "latest",
+        "version": "2025.1",
+        "url": "/scikit-learn-intelex/latest/"
+    },
+    {
+        "name": "2025.1",
+        "version": "2025.1",
+        "url": "/scikit-learn-intelex/2025.1/"
+    },
+    {
+        "name": "2025.0",
+        "version": "2025.0",
+        "url": "/scikit-learn-intelex/2025.0/"
+    },
+    {
+        "version": "2024.7",
+        "url": "/scikit-learn-intelex/2024.7/"
+    },
+    {
         "version": "2024.6",
-        "url": "https:///intel.github.io/scikit-learn-intelex/2024.6/"
+        "url": "/scikit-learn-intelex/2024.6/"
     },
     {
         "version": "2024.3",
-        "url": "https:///intel.github.io/scikit-learn-intelex/2024.3/"
-    }, 
+        "url": "/scikit-learn-intelex/2024.3/"
+    },
     {
         "version": "2023.2",
-        "url": "https:///intel.github.io/scikit-learn-intelex/2023.2/"
+        "url": "/scikit-learn-intelex/2023.2/"
     }
-]
\ No newline at end of file
+]